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| nonlinear regression matlab: Curve Fitting with MATLAB. Linear and Non Linear Regression. Interpolation Braselton J., 2016-06-21 Curve Fitting Toolbox(tm) provides an app and functions for fitting curves and surfaces to data. The toolbox lets you perform exploratory data analysis, preprocess and post-process data, compare candidate models, and remove outliers. You can conduct regression analysis using the library of linear and nonlinear models provided or specify your own custom equations. The library provides optimized solver parameters and starting conditions to improve the quality of your fits. The toolbox also supports nonparametric modeling techniques, such as splines, interpolation, and smoothing. |
| nonlinear regression matlab: Linear and Nonlinear Regression With Matlab. Fitting Curves and Surfaces to Data Perez C., 2017-08-17 MATLAB allows to work with linear and nonlinear regression models efficiently. It has tools that contemplate the phases of estimation, diagnosis and prediction.MATLAB Curve Fitting Toolbox lets you perform exploratory data analysis, preprocess and post-process data, compare candidate models, and remove outliers. You can conduct regression analysis using the library of linear and nonlinear models provided or specify your own custom equations. The library provides optimized solver parameters and starting conditions to improve the quality of your fits. The toolbox also supports nonparametric modeling techniques, such as splines, interpolation, and smoothing.After creating a fit, you can apply a variety of post-processing methods for plotting,interpolation, and extrapolation; estimating confidence intervals; and calculating integrals and derivatives.Curve Fitting Toolbox software allows you to work in two different environments:* An interactive environment, with the Curve Fitting app and the Spline Tool* A programmatic environment that allows you to write object-oriented MATLAB code using curve and surface fitting methodsThis book develops the following topics:* Curve Fitting * Surface Fitting * Spline Fitting * Parametric Fitting with Library Models * Polynomial Models * Exponential Models * Fourier Series Models* Gaussian Models* Power Series Models* Rational Models* Sum of Sines Models* Weibull Distribution Models* Least-Squares Fitting* Linear Least Squares * Weighted Least Squares * Robust Least Squares * Nonlinear Least Squares * Robust Fitting* Custom Linear and Nonlinear Regression * Nonparametric Fitting* Interpolation and Smoothing * Smoothing Splines* Filtering and Smoothing Data* Fit Postprocessing * Explore and Customize Plots * Remove Outliers * Select Validation Data * Evaluate a Curve Fit * Evaluate a Surface Fit* Compare Fits Programmatically * Evaluating Goodness of Fit* Residual Analysis * Confidence and Prediction Bounds |
| nonlinear regression matlab: Fitting Models to Biological Data Using Linear and Nonlinear Regression Harvey Motulsky, Arthur Christopoulos, 2004-05-27 Most biologists use nonlinear regression more than any other statistical technique, but there are very few places to learn about curve-fitting. This book, by the author of the very successful Intuitive Biostatistics, addresses this relatively focused need of an extraordinarily broad range of scientists. |
| nonlinear regression matlab: Econometrics With Matlab A. Smith, 2017-11-08 Statistics and Machine Learning Toolbox allows you to fit Nonlinear Regression Models. Once you fit a model, you can use it to predict or simulate responses, assess the model fit using hypothesis tests, or use plots to visualize diagnostics, residuals, and interaction effects. Parametric nonlinear models represent the relationship between a continuous response variable and one or more continuous predictor variables in the form y = f(X, b) + e, with f is a nonlinear function. fitnlm attempts to find values of the parameters b that minimize the mean squared differences between the observed responses y and the predictions of the model f(X, b). To do so, it needs a starting value beta0 before iteratively modifying the vector b to a vector with minimal mean squared error. This book develops nonlinear regression models taking into account the stages of identification, estimation, diagnosis and prediction. The most important content is the following: - Nonlinear Regression - Represent the Nonlinear Model - Choose Initial Vector beta0 - Fit Nonlinear Model to Data - Examine Quality and Adjust the Fitted Nonlinear Model - Predict or Simulate Responses Using a Nonlinear Model - Mixed-Effects Models - Introduction to Mixed-Effects Models - Mixed-Effects Model Hierarchy - Specifying Mixed-Effects Models - Specifying Covariate Models - Choosing nlmefit or nlmefitsa - Using Output Functions with Mixed-Effects Models - Examining Residuals for Model Verification - Mixed-Effects Models Using nlmefit and nlmefitsa - Multinomial Models for Nominal Responses - Multinomial Models for Ordinal Responses - Hierarchical Multinomial Models - Generalized Linear Models - Lasso Regularization of Generalized Linear Models - Regularize Poisson Regression - Regularize Logistic Regression - Regularize Wide Data in Parallel - Generalized Linear Mixed-Effects Models - Fit a Generalized Linear Mixed-Effects Model - Multivariate Generalized Linear Models - Multivariate Fixed Effects Panel Model with AutocorrelationMultivariate Longitudinal Analysis - Multivariate Longitudinal Analysis |
| nonlinear regression matlab: Statistics With Matlab L. Marvin, 2017-11-02 Linear regression models describe a linear relationship between a response and one or more predictive terms. Many times, however, a nonlinear relationship exists. Nonlinear Regression describes general nonlinear models. A special class of nonlinear models, called generalized linear models, uses linear methods.Parametric nonlinear models represent the relationship between a continuous response variable and one or more continuous predictor variables in the form y = f(X,b) + e, with f is a nonlinear function. fitnlm attempts to find values of the parameters b that minimize the mean squared differences between the observed responses y and the predictions of the model f(X,b). To do so, it needs a starting value beta0 before iteratively modifying the vector b to a vector with minimal mean squared error.Survival analysis consists of parametric, semiparametric, and nonparametric methods. You can use these to estimate the most commonly used measures in survival studies, survivor and hazard functions, compare them for different groups, and assess the relationship of predictor variables to survival time. Some statistical probability distributions describe survival times well. Commonly used distributions are exponential, Weibull, lognormal, Burr, and Birnbaum-Saunders distributions. Statistics and Machine Learning Toolbox functions ecdf and ksdensity compute the empirical and kernel density estimates of the cdf, cumulative hazard, and survivor functions. coxphfit fits the Cox proportional hazards model to the data.This book develops the Generalized Linear Models and Nonlinear regression ModelsThe most important content is the following:* Multinomial Models for Nominal Responses* Multinomial Models for Ordinal Responses* Hierarchical Multinomial Models* Generalized Linear Models* Lasso Regularization of Generalized Linear Models* Regularize Poisson Regression* Regularize Logistic Regression* Regularize Wide Data in Parallel* Generalized Linear Mixed-Effects Models* Fit a Generalized Linear Mixed-Effects Model* Nonlinear Regression* Represent the Nonlinear Model* Choose Initial Vector beta0* Fit Nonlinear Model to Data* Examine Quality and Adjust the Fitted Nonlinear Model* Predict or Simulate Responses Using a Nonlinear Model* Mixed-Effects Models* Introduction to Mixed-Effects Models* Mixed-Effects Model Hierarchy* Specifying Mixed-Effects Models* Specifying Covariate Models* Choosing nlmefit or nlmefitsa* Using Output Functions with Mixed-Effects Models* Examining Residuals for Model Verification* Mixed-Effects Models Using nlmefit and nlmefitsa* Survival Analysis* Kaplan-Meier Method* Hazard and Survivor Functions for Different Groups* Survivor Functions for Two Groups* Cox Proportional Hazards Model* Cox Proportional Hazards Model for Censored Data |
| nonlinear regression matlab: Nonlinear Regression Modeling for Engineering Applications R. Russell Rhinehart, 2016-08-01 Since mathematical models express our understanding of how nature behaves, we use them to validate our understanding of the fundamentals about systems (which could be processes, equipment, procedures, devices, or products). Also, when validated, the model is useful for engineering applications related to diagnosis, design, and optimization. First, we postulate a mechanism, then derive a model grounded in that mechanistic understanding. If the model does not fit the data, our understanding of the mechanism was wrong or incomplete. Patterns in the residuals can guide model improvement. Alternately, when the model fits the data, our understanding is sufficient and confidently functional for engineering applications. This book details methods of nonlinear regression, computational algorithms,model validation, interpretation of residuals, and useful experimental design. The focus is on practical applications, with relevant methods supported by fundamental analysis. This book will assist either the academic or industrial practitioner to properly classify the system, choose between the various available modeling options and regression objectives, design experiments to obtain data capturing critical system behaviors, fit the model parameters based on that data, and statistically characterize the resulting model. The author has used the material in the undergraduate unit operations lab course and in advanced control applications. |
| nonlinear regression matlab: CURVE and SURFACE FITTING with MATLAB. LINEAR and NONLINEAR REGRESSION A Ramirez, 2019-07-22 You can fit curves and surfaces to data and view plots with the Curve Fitting app in MATLAB. Is possible: .Create, plot, and compare multiple fits.Use linear or nonlinear regression, interpolation, smoothing, and custom equations..View goodness-of-fit statistics, display confidence intervals and residuals, remove outliers and assess fit with validation data..Automatically generate code to fit and plot curves and surfaces, or export fits to the workspace for further analysis.Curve Fitting app makes it easy to plot and analyze fit at the command line. You can export individual fit to the workspace for further analysis, or you can generate MATLAB code to recreate all fit and plots in your session. By generating code, you can use your interactive curve fitting session to quickly assemble code for curve and surface fit and plots into useful programs.The Curve Fitting app allows convenient, interactive use of Curve Fitting Toolbox functions, without programming. You can, however, access Curve Fitting Toolbox functions directly, and write programs that combine curve fitting functions with MATLAB functions and functions from other toolboxes. This allows you to create a curve fitting environment that is precisely suited to your needs. Models and fit in the Curve Fitting app are managed internally as curve fitting objects. Objects are manipulated through a variety of functions called methods. You can create curve fitting objects, and apply curve fitting methods, outside of the Curve Fitting app |
| nonlinear regression matlab: Introduction to Nonlinear Optimization Amir Beck, 2023-06-29 Built on the framework of the successful first edition, this book serves as a modern introduction to the field of optimization. The author’s objective is to provide the foundations of theory and algorithms of nonlinear optimization as well as to present a variety of applications from diverse areas of applied sciences. Introduction to Nonlinear Optimization gradually yet rigorously builds connections between theory, algorithms, applications, and actual implementation. The book contains several topics not typically included in optimization books, such as optimality conditions in sparsity constrained optimization, hidden convexity, and total least squares. Readers will discover a wide array of applications such as circle fitting, Chebyshev center, the Fermat–Weber problem, denoising, clustering, total least squares, and orthogonal regression. These applications are studied both theoretically and algorithmically, illustrating concepts such as duality. Python and MATLAB programs are used to show how the theory can be implemented. The extremely popular CVX toolbox (MATLAB) and CVXPY module (Python) are described and used. More than 250 theoretical, algorithmic, and numerical exercises enhance the reader's understanding of the topics. (More than 70 of the exercises provide detailed solutions, and many others are provided with final answers.) The theoretical and algorithmic topics are illustrated by Python and MATLAB examples. This book is intended for graduate or advanced undergraduate students in mathematics, computer science, electrical engineering, and potentially other engineering disciplines. |
| nonlinear regression matlab: Curve Fitting With Matlab J. Braselton, 2014-09-10 MATLAB Curve Fitting Toolbox provides graphical tools and command-line functions for fitting curves and surfaces to data. The toolbox lets you perform exploratory data analysis, preprocess and post-process data, compare candidate models, and remove outliers. You can conduct regression analysis using the library of linear and nonlinear models provided or specify your own custom equations. The library provides optimized solver parameters and starting conditions to improve the quality of your fits. The toolbox also supports nonparametric modeling techniques, such as splines, interpolation, and smoothing. After creating a fit, you can apply a variety of post-processing methods for plotting, interpolation, and extrapolation; estimating confidence intervals; and calculating integrals and derivatives. The most important topics in this book are: Linear and Nonlinear Regression Parametric Fitting Parametric Fitting with Library Models Selecting a Model Type Interactively Selecting Model Type Programmatically Using Normalize or Center and Scale Specifying Fit Options and Optimized Starting Points List of Library Models for Curve and Surface Fitting Use Library Models to Fit Data Library Model Types Model Names and Equations Polynomial Models About Polynomial Models Selecting a Polynomial Fit Interactively Selecting a Polynomial Fit at the Command Line Defining Polynomial Terms for Polynomial Surface Fits Exponential Models About Exponential Models Selecting an Exponential Fit Interactively Selecting an Exponential Fit at the Command Line Fourier Series About Fourier Series Models Selecting a Fourier Fit Interactively Selecting a Fourier Fit at the Command Line Gaussian Models About Gaussian Models Selecting a Gaussian Fit Interactively Selecting a Gaussian Fit at the Command Line Power Series About Power Series Models Selecting a Power Fit Interactively Selecting a Power Fit at the Command Line Rational Polynomials About Rational Models Selecting a Rational Fit Interactively Selecting a Rational Fit at the Command Line Sum of Sines Models About Sum of Sines Models Selecting a Sum of Sine Fit Interactively Selecting a Sum of Sine Fit at the Command Line Weibull Distributions About Weibull Distribution Models Selecting a Weibull Fit Interactively Selecting a Weibull Fit at the Command Line Least-Squares Fitting Introduction Error Distributions Linear Least Squares Weighted Least Squares Robust Least Squares Nonlinear Least Squares Custom Linear and Nonlinear Regression Interpolation and Smoothing Nonparametric Fitting Interpolants Interpolation Methods Selecting an Interpolant Fit Interactively Selecting an Interpolant Fit at the Command Line Smoothing Splines About Smoothing Splines Selecting a Smoothing Spline Fit Interactively Selecting a Smoothing Spline Fit at the Command Line Lowess Smoothing About Lowess Smoothing Selecting a Lowess Fit Interactively Selecting a Lowess Fit at the Command Line Fitting Automotive Fuel Efficiency Surfaces at the Command Line Filtering and Smoothing Data About Data Smoothing and Filtering Moving Average Filtering Savitzky-Golay Filtering Local Regression Smoothing Fit Postprocessing Exploring and Customizing Plots Displaying Fit and Residual Plots Viewing Surface Plots and Contour Plots Using Zoom, Pan, Data Cursor, and Outlier Exclusion Customizing the Fit Display Print to MATLAB Figures Removing Outliers Selecting Validation Data Generating Code and Exporting Fits to the Workspace Generating Code from the Curve Fitting Tool Exporting a Fit to the Workspace Evaluating Goodness of Fit How to Evaluate Goodness of Fit Goodness-of-Fit Statistics Residual Analysis Plotting and Analysing Residuals Confidence and Prediction Bounds About Confidence and Prediction Bounds Confidence Bounds on Coefficients Prediction Bounds on Fits Differentiating and Integrating a Fit Surface Fitting Objects and Methods |
| nonlinear regression matlab: Big Data Analytics With Matlab L. Marvin, 2017-09-10 MATLAB has the tools to work with large datasets and apply the necessary data analysis techniques. This book develops the work with nonlinear regression analysis techniques. Big data analytics examines large amounts of data to uncover hidden patterns, correlations and other insights. With today's technology, it's possible to analyze your data and get answers from it almost immediately - an effort that's slower and less efficient with more traditional business intelligence solutions. Nonlinear Regression models describe the nonlinear relationship between a response (output) variable, and one or more predictor (input) variables. Statistics and Machine Learning Toolbox allows you to fit linear, generalized linear, and nonlinear regression models, including stepwise models and mixed-effects models. Once you fit a model, you can use it to predict or simulate responses, assess the model fit using hypothesis tests, or use plots to visualize diagnostics, residuals, and interaction effects. Statistics and Machine Learning Toolbox also provides nonparametric regression methods to accommodate more complex regression curves without specifying the relationship between the response and the predictors with a predetermined regression function. You can predict responses for new data using the trained model. Gaussian process regression models also enable you to compute prediction intervals. |
| nonlinear regression matlab: Curve and Surface Fitting Peter Lancaster, Kȩstutis Šalkauskas, 1986 The purpose of this book is to reveal the foundations and major features of several basic methods for curve and surface fitting that are currently in use. |
| nonlinear regression matlab: Hydroinformatics Praveen Kumar, Mike Folk, Momcilo Markus, Jay C. Alameda, 2005-11-02 Modern hydrology is more interdisciplinary than ever. Staggering amounts and varieties of information pour in from GIS and remote sensing systems every day, and this information must be collected, interpreted, and shared efficiently. Hydroinformatics: Data Integrative Approaches in Computation, Analysis, and Modeling introduces the tools, approache |
| nonlinear regression matlab: Linear Programming with MATLAB Michael C. Ferris, Olvi L. Mangasarian, Stephen J. Wright, 2007-01-01 A self-contained introduction to linear programming using MATLAB® software to elucidate the development of algorithms and theory. Exercises are included in each chapter, and additional information is provided in two appendices and an accompanying Web site. Only a basic knowledge of linear algebra and calculus is required. |
| nonlinear regression matlab: Statistics for Chemical and Process Engineers Yuri A.W. Shardt, 2015-10-16 A coherent, concise and comprehensive course in the statistics needed for a modern career in chemical engineering; covers all of the concepts required for the American Fundamentals of Engineering examination. This book shows the reader how to develop and test models, design experiments and analyse data in ways easily applicable through readily available software tools like MS Excel® and MATLAB®. Generalized methods that can be applied irrespective of the tool at hand are a key feature of the text. The reader is given a detailed framework for statistical procedures covering: · data visualization; · probability; · linear and nonlinear regression; · experimental design (including factorial and fractional factorial designs); and · dynamic process identification. Main concepts are illustrated with chemical- and process-engineering-relevant examples that can also serve as the bases for checking any subsequent real implementations. Questions are provided (with solutions available for instructors) to confirm the correct use of numerical techniques, and templates for use in MS Excel and MATLAB can also be downloaded from extras.springer.com. With its integrative approach to system identification, regression and statistical theory, Statistics for Chemical and Process Engineers provides an excellent means of revision and self-study for chemical and process engineers working in experimental analysis and design in petrochemicals, ceramics, oil and gas, automotive and similar industries and invaluable instruction to advanced undergraduate and graduate students looking to begin a career in the process industries. |
| nonlinear regression matlab: Data-Driven Science and Engineering Steven L. Brunton, J. Nathan Kutz, 2022-05-05 A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®. |
| nonlinear regression matlab: Parameter Estimation and Inverse Problems Richard C. Aster, Brian Borchers, Clifford H. Thurber, 2018-10-16 Parameter Estimation and Inverse Problems, Third Edition, is structured around a course at New Mexico Tech and is designed to be accessible to typical graduate students in the physical sciences who do not have an extensive mathematical background. The book is complemented by a companion website that includes MATLAB codes that correspond to examples that are illustrated with simple, easy to follow problems that illuminate the details of particular numerical methods. Updates to the new edition include more discussions of Laplacian smoothing, an expansion of basis function exercises, the addition of stochastic descent, an improved presentation of Fourier methods and exercises, and more. - Features examples that are illustrated with simple, easy to follow problems that illuminate the details of a particular numerical method - Includes an online instructor's guide that helps professors teach and customize exercises and select homework problems - Covers updated information on adjoint methods that are presented in an accessible manner |
| nonlinear regression matlab: Process Dynamics and Control Dale E. Seborg, Thomas F. Edgar, Duncan A. Mellichamp, Francis J. Doyle, III, 2016-09-13 The new 4th edition of Seborg’s Process Dynamics Control provides full topical coverage for process control courses in the chemical engineering curriculum, emphasizing how process control and its related fields of process modeling and optimization are essential to the development of high-value products. A principal objective of this new edition is to describe modern techniques for control processes, with an emphasis on complex systems necessary to the development, design, and operation of modern processing plants. Control process instructors can cover the basic material while also having the flexibility to include advanced topics. |
| nonlinear regression matlab: Chemical Engineering Computation with MATLAB® Yeong Koo Yeo, 2020-12-15 Chemical Engineering Computation with MATLAB®, Second Edition continues to present basic to advanced levels of problem-solving techniques using MATLAB as the computation environment. The Second Edition provides even more examples and problems extracted from core chemical engineering subject areas and all code is updated to MATLAB version 2020. It also includes a new chapter on computational intelligence and: Offers exercises and extensive problem-solving instruction and solutions for various problems Features solutions developed using fundamental principles to construct mathematical models and an equation-oriented approach to generate numerical results Delivers a wealth of examples to demonstrate the implementation of various problem-solving approaches and methodologies for problem formulation, problem solving, analysis, and presentation, as well as visualization and documentation of results Includes an appendix offering an introduction to MATLAB for readers unfamiliar with the program, which will allow them to write their own MATLAB programs and follow the examples in the book Provides aid with advanced problems that are often encountered in graduate research and industrial operations, such as nonlinear regression, parameter estimation in differential systems, two-point boundary value problems and partial differential equations and optimization This essential textbook readies engineering students, researchers, and professionals to be proficient in the use of MATLAB to solve sophisticated real-world problems within the interdisciplinary field of chemical engineering. The text features a solutions manual, lecture slides, and MATLAB program files._ |
| nonlinear regression matlab: Computational Methods in Engineering S. P. Venkateshan, Prasanna Swaminathan, 2023-05-31 The book is designed to serve as a textbook for courses offered to graduate and upper-undergraduate students enrolled in mechanical engineering. The book attempts to make students with mathematical backgrounds comfortable with numerical methods. The book also serves as a handy reference for practicing engineers who are interested in applications. The book is written in an easy-to-understand manner, with the essence of each numerical method clearly stated. This makes it easy for professional engineers, students, and early career researchers to follow the material presented in the book. The structure of the book has been modeled accordingly. It is divided into four modules: i) solution of a system of equations and eigenvalues which includes linear equations, determining eigenvalues, and solution of nonlinear equations; ii) function approximations: interpolation, data fit, numerical differentiation, and numerical integration; iii) solution of ordinary differential equations—initial value problems and boundary value problems; and iv) solution of partial differential equations—parabolic, elliptic, and hyperbolic PDEs. Each section of the book includes exercises to reinforce the concepts, and problems have been added at the end of each chapter. Exercise problems may be solved by using computational tools such as scientific calculators, spreadsheet programs, and MATLAB codes. The detailed coverage and pedagogical tools make this an ideal textbook for students, early career researchers, and professionals. |
| nonlinear regression matlab: Systems Biology Andreas Kremling, 2013-11-12 Drawing on the latest research in the field, Systems Biology: Mathematical Modeling and Model Analysis presents many methods for modeling and analyzing biological systems, in particular cellular systems. It shows how to use predictive mathematical models to acquire and analyze knowledge about cellular systems. It also explores how the models are systematically applied in biotechnology. The first part of the book introduces biological basics, such as metabolism, signaling, gene expression, and control as well as mathematical modeling fundamentals, including deterministic models and thermodynamics. The text also discusses linear regression methods, explains the differences between linear and nonlinear regression, and illustrates how to determine input variables to improve estimation accuracy during experimental design. The second part covers intracellular processes, including enzymatic reactions, polymerization processes, and signal transduction. The author highlights the process–function–behavior sequence in cells and shows how modeling and analysis of signal transduction units play a mediating role between process and function. The third part presents theoretical methods that address the dynamics of subsystems and the behavior near a steady state. It covers techniques for determining different time scales, sensitivity analysis, structural kinetic modeling, and theoretical control engineering aspects, including a method for robust control. It also explores frequent patterns (motifs) in biochemical networks, such as the feed-forward loop in the transcriptional network of E. coli. Moving on to models that describe a large number of individual reactions, the last part looks at how these cellular models are used in biotechnology. The book also explains how graphs can illustrate the link between two components in large networks with several interactions. |
| nonlinear regression matlab: MATLAB for Machine Learning Giuseppe Ciaburro, 2017-08-24 Extract patterns and knowledge from your data in easy way using MATLABAbout This Book* Get your first steps into machine learning with the help of this easy-to-follow guide* Learn regression, clustering, classification, predictive analytics, artificial neural networks and more with MATLAB* Understand how your data works and identify hidden layers in the data with the power of machine learning.Who This Book Is ForThis book is for data analysts, data scientists, students, or anyone who is looking to get started with machine learning and want to build efficient data processing and predicting applications. A mathematical and statistical background will really help in following this book well.What You Will Learn* Learn the introductory concepts of machine learning.* Discover different ways to transform data using SAS XPORT, import and export tools,* Explore the different types of regression techniques such as simple & multiple linear regression, ordinary least squares estimation, correlations and how to apply them to your data.* Discover the basics of classification methods and how to implement Naive Bayes algorithm and Decision Trees in the Matlab environment.* Uncover how to use clustering methods like hierarchical clustering to grouping data using the similarity measures.* Know how to perform data fitting, pattern recognition, and clustering analysis with the help of MATLAB Neural Network Toolbox.* Learn feature selection and extraction for dimensionality reduction leading to improved performance.In DetailMATLAB is the language of choice for many researchers and mathematics experts for machine learning. This book will help you build a foundation in machine learning using MATLAB for beginners.You'll start by getting your system ready with t he MATLAB environment for machine learning and you'll see how to easily interact with the Matlab workspace. We'll then move on to data cleansing, mining and analyzing various data types in machine learning and you'll see how to display data values on a plot. Next, you'll get to know about the different types of regression techniques and how to apply them to your data using the MATLAB functions.You'll understand the basic concepts of neural networks and perform data fitting, pattern recognition, and clustering analysis. Finally, you'll explore feature selection and extraction techniques for dimensionality reduction for performance improvement.At the end of the book, you will learn to put it all together into real-world cases covering major machine learning algorithms and be comfortable in performing machine learning with MATLAB.Style and approachThe book takes a very comprehensive approach to enhance your understanding of machine learning using MATLAB. Sufficient real-world examples and use cases are included in the book to help you grasp the concepts quickly and apply them easily in your day-to-day work. |
| nonlinear regression matlab: Nonlinear Signal Processing Gonzalo R. Arce, 2005-01-03 Nonlinear Signal Processing: A Statistical Approach focuses on unifying the study of a broad and important class of nonlinear signal processing algorithms which emerge from statistical estimation principles, and where the underlying signals are non-Gaussian, rather than Gaussian, processes. Notably, by concentrating on just two non-Gaussian models, a large set of tools is developed that encompass a large portion of the nonlinear signal processing tools proposed in the literature over the past several decades. Key features include: * Numerous problems at the end of each chapter to aid development and understanding * Examples and case studies provided throughout the book in a wide range of applications bring the text to life and place the theory into context * A set of 60+ MATLAB software m-files allowing the reader to quickly design and apply any of the nonlinear signal processing algorithms described in the book to an application of interest is available on the accompanying FTP site. |
| nonlinear regression matlab: Applied Numerical Methods with MATLAB for Engineers and Scientists Steven C. Chapra, 2008 Still brief - but with the chapters that you wanted - Steven Chapra’s new second edition is written for engineering and science students who need to learn numerical problem solving. This text focuses on problem-solving applications rather than theory, using MATLAB throughout. Theory is introduced to inform key concepts which are framed in applications and demonstrated using MATLAB. The new second edition feature new chapters on Numerical Differentiation, Optimization, and Boundary-Value Problems (ODEs). |
| nonlinear regression matlab: Dynamic Mode Decomposition J. Nathan Kutz, Steven L. Brunton, Bingni W. Brunton, Joshua L. Proctor, 2016-11-23 Data-driven dynamical systems is a burgeoning field?it connects how measurements of nonlinear dynamical systems and/or complex systems can be used with well-established methods in dynamical systems theory. This is a critically important new direction because the governing equations of many problems under consideration by practitioners in various scientific fields are not typically known. Thus, using data alone to help derive, in an optimal sense, the best dynamical system representation of a given application allows for important new insights. The recently developed dynamic mode decomposition (DMD) is an innovative tool for integrating data with dynamical systems theory. The DMD has deep connections with traditional dynamical systems theory and many recent innovations in compressed sensing and machine learning. Dynamic Mode Decomposition: Data-Driven Modeling of Complex Systems, the first book to address the DMD algorithm, presents a pedagogical and comprehensive approach to all aspects of DMD currently developed or under development; blends theoretical development, example codes, and applications to showcase the theory and its many innovations and uses; highlights the numerous innovations around the DMD algorithm and demonstrates its efficacy using example problems from engineering and the physical and biological sciences; and provides extensive MATLAB code, data for intuitive examples of key methods, and graphical presentations. |
| nonlinear regression matlab: Data Analysis Using the Method of Least Squares John Wolberg, 2006-02-08 Develops the full power of the least-squares method Enables engineers and scientists to apply the method to their specific problem Deals with linear as well as with non-linear least-squares, parametric as well as non-parametric methods |
| nonlinear regression matlab: Introduction to Chemical Engineering Computing Bruce A. Finlayson, 2006-02-10 An innovative introduction to chemical engineering computing As chemical engineering technology advances, so does the complexity of the problems that arise. The problemsthat chemical engineers and chemical engineering students face today can no longer be answered with programs written on a case-by-case basis. Introduction to Chemical Engineering Computing teaches professionalsand students the kinds of problems they will have to solve, the types of computer programs needed to solve these problems, and how to ensure that the problems have been solved correctly. Each chapter in Introduction to Chemical Engineering Computing contains a description of the physicalproblem in general terms and in a mathematical context, thorough step-by-step instructions, numerous examples,and comprehensive explanations for each problem and program. This indispensable text features Excel,MATLAB(r), Aspen PlusTM, and FEMLAB programs and acquaints readers with the advantages of each. Perfect for students and professionals, Introduction to Chemical Engineering Computing gives readers the professional tools they need to solve real-world problems involving: * Equations of state * Vapor-liquid and chemical reaction equilibria * Mass balances with recycle streams * Mass transfer equipment * Process simulation * Chemical reactors * Transfer processes in 1D * Fluid flow in 2D and 3D * Convective diffusion equations in 2D and 3D |
| nonlinear regression matlab: An Introduction to Mathematical Modeling of Infectious Diseases Michael Y. Li, 2018-01-30 This text provides essential modeling skills and methodology for the study of infectious diseases through a one-semester modeling course or directed individual studies. The book includes mathematical descriptions of epidemiological concepts, and uses classic epidemic models to introduce different mathematical methods in model analysis. Matlab codes are also included for numerical implementations. It is primarily written for upper undergraduate and beginning graduate students in mathematical sciences who have an interest in mathematical modeling of infectious diseases. Although written in a rigorous mathematical manner, the style is not unfriendly to non-mathematicians. |
| nonlinear regression matlab: Applied Linear Statistical Models with Student CD Michael Kutner, Christopher Nachtsheim, John Neter, William Li, 2004-08-10 Applied Linear Statistical Models 5e is the long established leading authoritative text and reference on statistical modeling, analysis of variance, and the design of experiments. For students in most any discipline where statistical analysis or interpretation is used, ALSM serves as the standard work. The text proceeds through linear and nonlinear regression and modeling for the first half, and through ANOVA and Experimental Design in the second half. All topics are presented in a precise and clear style supported with solved examples, numbered formulae, graphic illustrations, and Comments to provide depth and statistical accuracy and precision. Applications used within the text and the hallmark problems, exercises, projects, and case studies are drawn from virtually all disciplines and fields providing motivation for students in virtually any college. The Fifth edition provides an increased use of computing and graphical analysis throughout, without sacrificing concepts or rigor. In general, the 5e uses larger data sets in examples and exercises, and the use of automated software without loss of understanding. |
| nonlinear regression matlab: Statistical Models and Methods for Financial Markets Tze Leung Lai, Haipeng Xing, 2008-07-25 The idea of writing this bookarosein 2000when the ?rst author wasassigned to teach the required course STATS 240 (Statistical Methods in Finance) in the new M. S. program in ?nancial mathematics at Stanford, which is an interdisciplinary program that aims to provide a master’s-level education in applied mathematics, statistics, computing, ?nance, and economics. Students in the programhad di?erent backgroundsin statistics. Some had only taken a basic course in statistical inference, while others had taken a broad spectrum of M. S. - and Ph. D. -level statistics courses. On the other hand, all of them had already taken required core courses in investment theory and derivative pricing, and STATS 240 was supposed to link the theory and pricing formulas to real-world data and pricing or investment strategies. Besides students in theprogram,thecoursealso attractedmanystudentsfromother departments in the university, further increasing the heterogeneity of students, as many of them had a strong background in mathematical and statistical modeling from the mathematical, physical, and engineering sciences but no previous experience in ?nance. To address the diversity in background but common strong interest in the subject and in a potential career as a “quant” in the ?nancialindustry,thecoursematerialwascarefullychosennotonlytopresent basic statistical methods of importance to quantitative ?nance but also to summarize domain knowledge in ?nance and show how it can be combined with statistical modeling in ?nancial analysis and decision making. The course material evolved over the years, especially after the second author helped as the head TA during the years 2004 and 2005. |
| nonlinear regression matlab: Numerical Methods for Chemical Engineers Using Excel, VBA, and MATLAB Victor J. Law, 2013-03-05 While teaching the Numerical Methods for Engineers course over the last 15 years, the author found a need for a new textbook, one that was less elementary, provided applications and problems better suited for chemical engineers, and contained instruction in Visual Basic for Applications (VBA). This led to six years of developing teaching notes that |
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| nonlinear regression matlab: Learning and Soft Computing Vojislav Kecman, 2001 This textbook provides a thorough introduction to the field of learning from experimental data and soft computing. Support vector machines (SVM) and neural networks (NN) are the mathematical structures, or models, that underlie learning, while fuzzy logic systems (FLS) enable us to embed structured human knowledge into workable algorithms. The book assumes that it is not only useful, but necessary, to treat SVM, NN, and FLS as parts of a connected whole. Throughout, the theory and algorithms are illustrated by practical examples, as well as by problem sets and simulated experiments. This approach enables the reader to develop SVM, NN, and FLS in addition to understanding them. The book also presents three case studies: on NN-based control, financial time series analysis, and computer graphics. A solutions manual and all of the MATLAB programs needed for the simulated experiments are available. |
| nonlinear regression matlab: Nonlinear System Identification Stephen A. Billings, 2013-07-29 Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio-Temporal Domains describes a comprehensive framework for the identification and analysis of nonlinear dynamic systems in the time, frequency, and spatio-temporal domains. This book is written with an emphasis on making the algorithms accessible so that they can be applied and used in practice. Includes coverage of: The NARMAX (nonlinear autoregressive moving average with exogenous inputs) model The orthogonal least squares algorithm that allows models to be built term by term where the error reduction ratio reveals the percentage contribution of each model term Statistical and qualitative model validation methods that can be applied to any model class Generalised frequency response functions which provide significant insight into nonlinear behaviours A completely new class of filters that can move, split, spread, and focus energy The response spectrum map and the study of sub harmonic and severely nonlinear systems Algorithms that can track rapid time variation in both linear and nonlinear systems The important class of spatio-temporal systems that evolve over both space and time Many case study examples from modelling space weather, through identification of a model of the visual processing system of fruit flies, to tracking causality in EEG data are all included to demonstrate how easily the methods can be applied in practice and to show the insight that the algorithms reveal even for complex systems NARMAX algorithms provide a fundamentally different approach to nonlinear system identification and signal processing for nonlinear systems. NARMAX methods provide models that are transparent, which can easily be analysed, and which can be used to solve real problems. This book is intended for graduates, postgraduates and researchers in the sciences and engineering, and also for users from other fields who have collected data and who wish to identify models to help to understand the dynamics of their systems. |
| nonlinear regression matlab: 26th European Symposium on Computer Aided Process Engineering , 2016-06-17 26th European Symposium on Computer Aided Process Engineering contains the papers presented at the 26th European Society of Computer-Aided Process Engineering (ESCAPE) Event held at Portorož Slovenia, from June 12th to June 15th, 2016. Themes discussed at the conference include Process-product Synthesis, Design and Integration, Modelling, Numerical analysis, Simulation and Optimization, Process Operations and Control and Education in CAPE/PSE. - Presents findings and discussions from the 26th European Society of Computer-Aided Process Engineering (ESCAPE) Event |
| nonlinear regression matlab: Machine Learning Methods in the Environmental Sciences William W. Hsieh, 2009-07-30 A graduate textbook that provides a unified treatment of machine learning methods and their applications in the environmental sciences. |
| nonlinear regression matlab: Monte Carlo Methods in Fuzzy Optimization James J. Buckley, Leonard J. Jowers, 2008-02-20 Monte Carlo Methods in Fuzzy Optimization is a clear and didactic book about Monte Carlo methods using random fuzzy numbers to obtain approximate solutions to fuzzy optimization problems. The book includes various solved problems such as fuzzy linear programming, fuzzy regression, fuzzy inventory control, fuzzy game theory, and fuzzy queuing theory. The book will appeal to engineers, researchers, and students in Fuzziness and applied mathematics. |
| nonlinear regression matlab: Parallel Computing for Bioinformatics and Computational Biology Albert Y. Zomaya, 2006-04-21 Discover how to streamline complex bioinformatics applications with parallel computing This publication enables readers to handle more complex bioinformatics applications and larger and richer data sets. As the editor clearly shows, using powerful parallel computing tools can lead to significant breakthroughs in deciphering genomes, understanding genetic disease, designing customized drug therapies, and understanding evolution. A broad range of bioinformatics applications is covered with demonstrations on how each one can be parallelized to improve performance and gain faster rates of computation. Current parallel computing techniques and technologies are examined, including distributed computing and grid computing. Readers are provided with a mixture of algorithms, experiments, and simulations that provide not only qualitative but also quantitative insights into the dynamic field of bioinformatics. Parallel Computing for Bioinformatics and Computational Biology is a contributed work that serves as a repository of case studies, collectively demonstrating how parallel computing streamlines difficult problems in bioinformatics and produces better results. Each of the chapters is authored by an established expert in the field and carefully edited to ensure a consistent approach and high standard throughout the publication. The work is organized into five parts: * Algorithms and models * Sequence analysis and microarrays * Phylogenetics * Protein folding * Platforms and enabling technologies Researchers, educators, and students in the field of bioinformatics will discover how high-performance computing can enable them to handle more complex data sets, gain deeper insights, and make new discoveries. |
| nonlinear regression matlab: Human-in-the-loop System Design and Control Adaptation for Behavior-Assistant Robots Yuquan Leng, Ningbo Yu, Bi Zhang, Benyan Huo , Xu Jin, 2024-06-03 With the progress and development of human-robot systems, the coordination among humans, robots, and environments has become increasingly sophisticated. In this Research Topic, we focus on an important field in robotics and automation disciplines, which is commonly defined as behavior-assistant robots. The scope includes but is not limited to: (1) rehabilitation assistive devices, such as rigid/soft exoskeletons, prosthetic systems, orthoses, and intelligent wheelchairs; (2) intelligent medical systems, such as endoscopic robots, surgical robots, and the navigation systems; (3) industrial application devices, such as collaborative manipulators, load-bearing exoskeletons, supernumerary robotic limbs; (4) intelligent domestic devices, such as mobile robots, elderly-care robots, walking-aids robots and so on. The emergence of robot-assisted daily behaviors, based on aforementioned devices, is gradually becoming part of our social lives, which can improve weak motor abilities, enhance physical functionalities, and enable various other benefits. |
| nonlinear regression matlab: Applied Numerical Methods for Chemical Engineers Navid Mostoufi, Alkis Constantinides, 2022-05-22 Applied Numerical Methods for Chemical Engineers emphasizes the derivation of a variety of numerical methods and their application to the solution of engineering problems, with special attention to problems in the chemical engineering field. These algorithms encompass linear and nonlinear algebraic equations, eigenvalue problems, finite difference methods, interpolation, differentiation and integration, ordinary differential equations, boundary value problems, partial differential equations, and linear and nonlinear regression analysis. MATLAB is adopted as the calculation environment throughout the book because of its ability to perform all the calculations in matrix form, its large library of built-in functions, its strong structural language, and its rich graphical visualization tools. Through this book, students and other users will learn about the basic features, advantages and disadvantages of various numerical methods, learn and practice many useful m-files developed for different numerical methods in addition to the MATLAB built-in solvers, develop and set up mathematical models for problems commonly encountered in chemical engineering, and solve chemical engineering related problems through examples and after-chapter problems with MATLAB by creating application m-files. - Clearly and concisely develops a variety of numerical methods and applies them to the solution of chemical engineering problems. These algorithms encompass linear and nonlinear algebraic equations, eigenvalue problems, finite difference methods, interpolation, linear and nonlinear regression analysis, differentiation and integration, ordinary differential equations, boundary value problems, and partial differential equations - Includes systematic development of the calculus of finite differences and its application to the integration of differential equations, and a detailed discussion of nonlinear regression analysis, with powerful programs for implementing multivariable nonlinear regression and statistical analysis of the results - Makes extensive use of MATLAB and Excel, with most of the methods discussed implemented into general MATLAB functions. All the MATLAB-language scripts developed are listed in the text and included in the book's companion website - Includes numerous real-world examples and homework problems drawn from the field of chemical and biochemical engineering |
| nonlinear regression matlab: Light Metals 2013 Barry Sadler, 2017-08-18 |
Home | Nonlinear Dynamics - Springer
Nonlinear Dynamics is a hybrid journal publishing original content at the forefront of nonlinear dynamic research across diverse systems and scales. The journal covers nonlinear dynamics …
Methods in Nonlinear Analysis - SpringerLink
Theories, techniques and results in many different branches of mathematics have been combined in solving nonlinear problems. This book collects and reorganizes up-to-date materials …
Nonlinear Acoustics - SpringerLink
Chapters 10 through 15 cover applications and additional methodologies encountered in nonlinear acoustics that include perturbation and numerical methods, ray theory for inhomogeneous …
Home | Journal of Nonlinear Science - Springer
The mission of the Journal of Nonlinear Science is to publish papers that augment the fundamental ways we describe, model, and predict nonlinear phenomena. It features papers …
Nonlinear Systems: Analysis, Stability, and Control | SpringerLink
Coupled with this set of analytic advances has been the vast increase in computational power available for both the simulation and visualization of nonlinear systems as well as for the …
Articles | Nonlinear Dynamics - Springer
4 days ago · Nonlinear Dynamics is a hybrid journal publishing original content at the forefront of nonlinear dynamic research across diverse systems and scales. The ...
Nonlinear Dynamics: A Concise Introduction Interlaced with Code ...
This concise and up-to-date textbook provides an accessible introduction to the core concepts of nonlinear dynamics as well as its existing and potential applications. The book is aimed at …
Data-driven nonlinear and stochastic dynamics with control
Dec 16, 2024 · The analysis is developed with reference to a nonlinear beam where the two boundary conditions have nonlinearities and masses, with the goal of identifying the uncertain …
Lectures on Nonlinear Dynamics - SpringerLink
This book presents a compilation of lectures delivered at the São Paulo School of Advanced Sciences on Nonlinear Dynamics, categorized into four groups: parametric resonance, …
Aims and scope | Nonlinear Dynamics - Springer
Nonlinear Dynamics provides a forum for the rapid publication of original research in the field of nonlinear dynamics. The scope of the journal encompasses all nonlinear dynamic phenomena …
Home | Nonlinear Dynamics - Springer
Nonlinear Dynamics is a hybrid journal publishing original content at the forefront of nonlinear dynamic research across diverse systems and scales. The journal covers nonlinear dynamics …
Methods in Nonlinear Analysis - SpringerLink
Theories, techniques and results in many different branches of mathematics have been combined in solving nonlinear problems. This book collects and reorganizes up-to-date materials …
Nonlinear Acoustics - SpringerLink
Chapters 10 through 15 cover applications and additional methodologies encountered in nonlinear acoustics that include perturbation and numerical methods, ray theory for inhomogeneous …
Home | Journal of Nonlinear Science - Springer
The mission of the Journal of Nonlinear Science is to publish papers that augment the fundamental ways we describe, model, and predict nonlinear phenomena. It features papers …
Nonlinear Systems: Analysis, Stability, and Control | SpringerLink
Coupled with this set of analytic advances has been the vast increase in computational power available for both the simulation and visualization of nonlinear systems as well as for the …
Articles | Nonlinear Dynamics - Springer
4 days ago · Nonlinear Dynamics is a hybrid journal publishing original content at the forefront of nonlinear dynamic research across diverse systems and scales. The ...
Nonlinear Dynamics: A Concise Introduction Interlaced with Code ...
This concise and up-to-date textbook provides an accessible introduction to the core concepts of nonlinear dynamics as well as its existing and potential applications. The book is aimed at …
Data-driven nonlinear and stochastic dynamics with control
Dec 16, 2024 · The analysis is developed with reference to a nonlinear beam where the two boundary conditions have nonlinearities and masses, with the goal of identifying the uncertain …
Lectures on Nonlinear Dynamics - SpringerLink
This book presents a compilation of lectures delivered at the São Paulo School of Advanced Sciences on Nonlinear Dynamics, categorized into four groups: parametric resonance, …
Aims and scope | Nonlinear Dynamics - Springer
Nonlinear Dynamics provides a forum for the rapid publication of original research in the field of nonlinear dynamics. The scope of the journal encompasses all nonlinear dynamic phenomena …
