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| tsukamoto fuzzy model: Type-2 Fuzzy Logic: Theory and Applications Oscar Castillo, Patricia Melin, 2008-02-20 This book describes new methods for building intelligent systems using type-2 fuzzy logic and soft computing (SC) techniques. The authors extend the use of fuzzy logic to a higher order, which is called type-2 fuzzy logic. Combining type-2 fuzzy logic with traditional SC techniques, we can build powerful hybrid intelligent systems that can use the advantages that each technique offers. This book is intended to be a major reference tool and can be used as a textbook. |
| tsukamoto fuzzy model: Hybrid Intelligent Systems for Pattern Recognition Using Soft Computing Patricia Melin, Oscar Castillo, 2005-03-08 This monograph describes new methods for intelligent pattern recognition using soft computing techniques including neural networks, fuzzy logic, and genetic algorithms. Hybrid intelligent systems that combine several soft computing techniques are needed due to the complexity of pattern recognition problems. Hybrid intelligent systems can have different architectures, which have an impact on the efficiency and accuracy of pattern recognition systems, to achieve the ultimate goal of pattern recognition. This book also shows results of the application of hybrid intelligent systems to real-world problems of face, fingerprint, and voice recognition. This monograph is intended to be a major reference for scientists and engineers applying new computational and mathematical tools to intelligent pattern recognition and can be also used as a textbook for graduate courses in soft computing, intelligent pattern recognition, computer vision, or applied artificial intelligence. |
| tsukamoto fuzzy model: Fuzzy Logic with Engineering Applications Timothy J. Ross, 2005-04-08 Fuzzy logic refers to a large subject dealing with a set of methods to characterize and quantify uncertainty in engineering systems that arise from ambiguity, imprecision, fuzziness, and lack of knowledge. Fuzzy logic is a reasoning system based on a foundation of fuzzy set theory, itself an extension of classical set theory, where set membership can be partial as opposed to all or none, as in the binary features of classical logic. Fuzzy logic is a relatively new discipline in which major advances have been made over the last decade or so with regard to theory and applications. Following on from the successful first edition, this fully updated new edition is therefore very timely and much anticipated. Concentration on the topics of fuzzy logic combined with an abundance of worked examples, chapter problems and commercial case studies is designed to help motivate a mainstream engineering audience, and the book is further strengthened by the inclusion of an online solutions manual as well as dedicated software codes. Senior undergraduate and postgraduate students in most engineering disciplines, academics and practicing engineers, plus some working in economics, control theory, operational research etc, will all find this a valuable addition to their bookshelves. |
| tsukamoto fuzzy model: Nature-Inspired Optimization Algorithms for Fuzzy Controlled Servo Systems Radu-Emil Precup, Radu-Codrut David, 2019-04-23 Nature-inspired Optimization Algorithms for Fuzzy Controlled Servo Systems explains fuzzy control in servo systems in a way that doesn't require any solid mathematical prerequisite. Analysis and design methodologies are covered, along with specific applications to servo systems and representative case studies. The theoretical approaches presented throughout the book are validated by the illustration of digital simulation and real-time experimental results. This book is a great resource for a wide variety of readers, including graduate students, engineers (designers, practitioners and researchers), and everyone who faces challenging control problems. |
| tsukamoto fuzzy model: Neuro-Fuzzy and Soft Computing Mr. Rohit Manglik, 2024-04-06 EduGorilla Publication is a trusted name in the education sector, committed to empowering learners with high-quality study materials and resources. Specializing in competitive exams and academic support, EduGorilla provides comprehensive and well-structured content tailored to meet the needs of students across various streams and levels. |
| tsukamoto fuzzy model: Introduction to Fuzzy Logic using MATLAB S.N. Sivanandam, S. Sumathi, S. N. Deepa, 2006-10-28 Fuzzy Logic, at present is a hot topic, among academicians as well various programmers. This book is provided to give a broad, in-depth overview of the field of Fuzzy Logic. The basic principles of Fuzzy Logic are discussed in detail with various solved examples. The different approaches and solutions to the problems given in the book are well balanced and pertinent to the Fuzzy Logic research projects. The applications of Fuzzy Logic are also dealt to make the readers understand the concept of Fuzzy Logic. The solutions to the problems are programmed using MATLAB 6.0 and the simulated results are given. The MATLAB Fuzzy Logic toolbox is provided for easy reference. |
| tsukamoto fuzzy model: Fuzzy Systems Ahmad Taher Azar, 2010-02-01 While several books are available today that address the mathematical and philosophical foundations of fuzzy logic, none, unfortunately, provides the practicing knowledge engineer, system analyst, and project manager with specific, practical information about fuzzy system modeling. Those few books that include applications and case studies concentrate almost exclusively on engineering problems: pendulum balancing, truck backeruppers, cement kilns, antilock braking systems, image pattern recognition, and digital signal processing. Yet the application of fuzzy logic to engineering problems represents only a fraction of its real potential. As a method of encoding and using human knowledge in a form that is very close to the way experts think about difficult, complex problems, fuzzy systems provide the facilities necessary to break through the computational bottlenecks associated with traditional decision support and expert systems. Additionally, fuzzy systems provide a rich and robust method of building systems that include multiple conflicting, cooperating, and collaborating experts (a capability that generally eludes not only symbolic expert system users but analysts who have turned to such related technologies as neural networks and genetic algorithms). Yet the application of fuzzy logic in the areas of decision support, medical systems, database analysis and mining has been largely ignored by both the commercial vendors of decision support products and the knowledge engineers who use them. |
| tsukamoto fuzzy model: Methodologies For The Conception, Design, And Application Of Intelligent Systems - Proceedings Of The 4th International Conference On Soft Computing (In 2 Volumes) Gen Matsumoto, Takeshi Yamakawa, 1996-08-31 IIZUKA '96, the 4th International Conference on Soft Computing, emphasized the integration of the components of soft computing to promote the research work on post-digital computers and to realize the intelligent systems. At the conference, new developments and results in soft computing were introduced and discussed by researchers from academic, governmental, and industrial institutions.This volume presents the opening lectures by Prof. Lotfi A. Zadeh and Prof. Walter J. Freeman, the plenary lectures by seven eminent researchers, and about 200 carefully selected papers drawn from more than 20 countries. It documents current research and in-depth studies on the conception, design, and application of intelligent systems. |
| tsukamoto fuzzy model: Fuzzy Expert Systems for Disease Diagnosis Kumar, A.V. Senthil, 2014-11-30 The development of fuzzy expert systems has provided new opportunities for problem solving amidst uncertainties. The medical field, in particular, has benefitted tremendously from advancing fuzzy system technologies. Fuzzy Expert Systems for Disease Diagnosis highlights the latest research and developments in fuzzy rule-based methods used in the detection of medical complications and illness. Offering emerging solutions and practical applications, this timely publication is designed for use by researchers, academicians, and students, as well as practitioners in the medical field. |
| tsukamoto fuzzy model: Modelling, Simulation and Control of Non-linear Dynamical Systems Patricia Melin, Oscar Castillo, 2001-10-25 These authors use soft computing techniques and fractal theory in this new approach to mathematical modeling, simulation and control of complexion-linear dynamical systems. First, a new fuzzy-fractal approach to automated mathematical modeling of non-linear dynamical systems is presented. It is illustrated with examples on the PROLOG programming la |
| tsukamoto fuzzy model: Supply Chain Management Under Fuzziness Cengiz Kahraman, Başar Öztayşi, 2014-02-15 Supply Chain Management Under Fuzziness presents recently developed fuzzy models and techniques for supply chain management. These include: fuzzy PROMETHEE, fuzzy AHP, fuzzy ANP, fuzzy VIKOR, fuzzy DEMATEL, fuzzy clustering, fuzzy linear programming, and fuzzy inference systems. The book covers both practical applications and new developments concerning these methods. This book offers an excellent resource for researchers and practitioners in supply chain management and logistics, and will provide them with new suggestions and directions for future research. Moreover, it will support graduate students in their university courses, such as specialized courses on supply chains and logistics, as well as related courses in the fields of industrial engineering, engineering management and business administration. |
| tsukamoto fuzzy model: Cyber-Physical Systems: Intelligent Models and Algorithms Alla G. Kravets, Alexander A. Bolshakov, Maxim Shcherbakov, 2022-03-29 This book is devoted to intelligent models and algorithms as the core components of cyber-physical systems. The complexity of cyber-physical systems developing and deploying requires new approaches to its modelling and design. Presents results in the field of modelling technologies that leverage the exploitation of artificial intelligence, including artificial general intelligence (AGI) and weak artificial intelligence. Provides scientific, practical, and methodological approaches based on bio-inspired methods, fuzzy models and algorithms, predictive modelling, computer vision and image processing. The target audience of the book are practitioners, enterprises representatives, scientists, PhD and Master students who perform scientific research or applications of intelligent models and algorithms in cyber-physical systems for various domains. |
| tsukamoto fuzzy model: Soft Computing and Fractal Theory for Intelligent Manufacturing Oscar Castillo, Patricia Melin, 2012-08-11 We describe in this book, new methods for intelligent manufacturing using soft computing techniques and fractal theory. Soft Computing (SC) consists of several computing paradigms, including fuzzy logic, neural networks, and genetic algorithms, which can be used to produce powerful hybrid intelligent systems. Fractal theory provides us with the mathematical tools to understand the geometrical complexity of natural objects and can be used for identification and modeling purposes. Combining SC techniques with fractal theory, we can take advantage of the intelligence provided by the computer methods and also take advantage of the descriptive power of the fractal mathematical tools. Industrial manufacturing systems can be considered as non-linear dynamical systems, and as a consequence can have highly complex dynamic behaviors. For this reason, the need for computational intelligence in these manufacturing systems has now been well recognized. We consider in this book the concept of intelligent manufacturing as the application of soft computing techniques and fractal theory for achieving the goals of manufacturing, which are production planning and control, monitoring and diagnosis of faults, and automated quality control. As a prelude, we provide a brief overview of the existing methodologies in Soft Computing. We then describe our own approach in dealing with the problems in achieving intelligent manufacturing. Our particular point of view is that to really achieve intelligent manufacturing in real-world applications we need to use SC techniques and fractal theory. |
| tsukamoto fuzzy model: Knowledge is Power in Four Dimensions: Models to Forecast Future Paradigm Bahman Zohuri, Farhang Mossavar Rahmani, Farahnaz Behgounia, 2022-07-14 Knowledge is Power in Four Dimensions: Models to Forecast Future Paradigms, Forecasting Energy for Tomorrow's World with Mathematical Modeling and Python Programming Driven Artificial Intelligence delivers knowledge on key infrastructure topics in both AI technology and energy. Sections lay the groundwork for tomorrow's computing functionality, starting with how to build a Business Resilience System (BRS), data warehousing, data management, and fuzzy logic. Subsequent chapters dive into the impact of energy on economic development and the environment and mathematical modeling, including energy forecasting and engineering statistics. Energy examples are included for application and learning opportunities. A final section deliver the most advanced content on artificial intelligence with the integration of machine learning and deep learning as a tool to forecast and make energy predictions. The reference covers many introductory programming tools, such as Python, Scikit, TensorFlow and Kera. - Helps users gain fundamental knowledge in technology infrastructure, including AI, machine learning and fuzzy logic - Compartmentalizes data knowledge into near-term and long-term forecasting models, with examples involving both renewable and non-renewable energy outcomes - Advances climate resiliency and helps readers build a business resiliency system for assets |
| tsukamoto fuzzy model: Intelligent Machines Clarence W. de Silva, 2000-06-22 What is intelligence? Are truly intelligent machines a practical reality? If so, can they work in harmony with human beings and improve the quality of our lives? How are they designed, built, and controlled? The fact is that machines with brains are no longer the stuff of science fiction. Research focused on developing smarter, more flexible |
| tsukamoto fuzzy model: Computational Intelligence Mr. Rohit Manglik, 2024-03-08 EduGorilla Publication is a trusted name in the education sector, committed to empowering learners with high-quality study materials and resources. Specializing in competitive exams and academic support, EduGorilla provides comprehensive and well-structured content tailored to meet the needs of students across various streams and levels. |
| tsukamoto fuzzy model: Terrigenous Mass Movements Biswajeet Pradhan, Manfred Buchroithner, 2012-04-02 Terrestrial mass movements (i.e. cliff collapses, soil creeps, mudflows, landslides etc.) are severe forms of natural disasters mostly occurring in mountainous terrain, which is subjected to specific geological, geomorphological and climatological conditions, as well as to human activities. It is a challenging task to accurately define the position, type and activity of mass movements for the purpose of creating inventory records and potential vulnerability maps. Remote sensing techniques, in combination with Geographic Information System tools, allow state-of-the-art investigation of the degree of potential mass movements and modeling surface processes for hazard and risk mapping. Similarly, through statistical prediction models, future mass-movement-prone areas can be identified and damages can to a certain extent be minimized. Issues of scale and selection of morphological attributes for the scientific analysis of mass movements call for new developments in data modeling and spatio-temporal GIS analysis. The book is a product of a cooperation between the editors and several contributing authors, addressing current issues and recent developments in GI technology and mass movements research. Its fundamental treatment of this technology includes data modeling, topography, geology, geomorphology, remote sensing, artificial neural networks, binomial regression, fuzzy logic, spatial statistics and analysis, and scientific visualization. Both theoretical and practical issues are addressed. |
| tsukamoto fuzzy model: Computational Intelligence Paradigms S. Sumathi, Surekha Paneerselvam, 2010-01-05 Offering a wide range of programming examples implemented in MATLAB, Computational Intelligence Paradigms: Theory and Applications Using MATLAB presents theoretical concepts and a general framework for computational intelligence (CI) approaches, including artificial neural networks, fuzzy systems, evolutionary computation, genetic algorithms and pr |
| tsukamoto fuzzy model: Intelligent Systems Crina Grosan, Ajith Abraham, 2011-07-29 Computational intelligence is a well-established paradigm, where new theories with a sound biological understanding have been evolving. The current experimental systems have many of the characteristics of biological computers (brains in other words) and are beginning to be built to perform a variety of tasks that are difficult or impossible to do with conventional computers. As evident, the ultimate achievement in this field would be to mimic or exceed human cognitive capabilities including reasoning, recognition, creativity, emotions, understanding, learning and so on. This book comprising of 17 chapters offers a step-by-step introduction (in a chronological order) to the various modern computational intelligence tools used in practical problem solving. Staring with different search techniques including informed and uninformed search, heuristic search, minmax, alpha-beta pruning methods, evolutionary algorithms and swarm intelligent techniques; the authors illustrate the design of knowledge-based systems and advanced expert systems, which incorporate uncertainty and fuzziness. Machine learning algorithms including decision trees and artificial neural networks are presented and finally the fundamentals of hybrid intelligent systems are also depicted. Academics, scientists as well as engineers engaged in research, development and application of computational intelligence techniques, machine learning and data mining would find the comprehensive coverage of this book invaluable. |
| tsukamoto fuzzy model: Modeling and Simulation of Dynamical Systems Payam Zarafshan, 2024-11-27 Modeling and Simulation of Dynamical Systems explores the common methods used in the modeling and simulation of dynamic systems, providing foundational information that is essential for further research. A key feature of this title is its systematic separation and classification of various modeling methods, enabling readers to select their preferred approach after studying the initial chapter and becoming familiar with fundamental definitions. Another unique feature is the use of numerous examples and solved problems throughout the book to support a basic understanding of a system's behavior.This title is highly recommended for researchers, professionals, and students in mechanical, biosystems, and mechatronic engineering. - Explores, in detail, the different methods of modeling dynamic systems - Provides numerous examples and solved problems, which distinguishes this book from other reference titles in the field - Renders information on modeling and simulating software |
| tsukamoto fuzzy model: Proceedings of the Second International Conference on Soft Computing for Problem Solving (SocProS 2012), December 28-30, 2012 B. V. Babu, Atulya Nagar, Kusum Deep, Millie Pant, Jagdish Chand Bansal, Kanad Ray, Umesh Gupta, 2014-07-08 The present book is based on the research papers presented in the International Conference on Soft Computing for Problem Solving (SocProS 2012), held at JK Lakshmipat University, Jaipur, India. This book provides the latest developments in the area of soft computing and covers a variety of topics, including mathematical modeling, image processing, optimization, swarm intelligence, evolutionary algorithms, fuzzy logic, neural networks, forecasting, data mining, etc. The objective of the book is to familiarize the reader with the latest scientific developments that are taking place in various fields and the latest sophisticated problem solving tools that are being developed to deal with the complex and intricate problems that are otherwise difficult to solve by the usual and traditional methods. The book is directed to the researchers and scientists engaged in various fields of Science and Technology. |
| tsukamoto fuzzy model: System and Circuit Design for Biologically-Inspired Intelligent Learning Temel, Turgay, 2010-10-31 The objective of the book is to introduce and bring together well-known circuit design aspects, as well as to cover up-to-date outcomes of theoretical studies in decision-making, biologically-inspired, and artificial intelligent learning techniques--Provided by publisher. |
| tsukamoto fuzzy model: Robust Industrial Control Systems Michael J. Grimble, 2006-05-01 Robust Industrial Control Systems: Optimal Design Approach for Polynomial Systems presents a comprehensive introduction to the use of frequency domain and polynomial system design techniques for a range of industrial control and signal processing applications. The solution of stochastic and robust optimal control problems is considered, building up from single-input problems and gradually developing the results for multivariable design of the later chapters. In addition to cataloguing many of the results in polynomial systems needed to calculate industrial controllers and filters, basic design procedures are also introduced which enable cost functions and system descriptions to be specified in order to satisfy industrial requirements. Providing a range of solutions to control and signal processing problems, this book: * Presents a comprehensive introduction to the polynomial systems approach for the solution of H_2 and H_infinity optimal control problems. * Develops robust control design procedures using frequency domain methods. * Demonstrates design examples for gas turbines, marine systems, metal processing, flight control, wind turbines, process control and manufacturing systems. * Includes the analysis of multi-degrees of freedom controllers and the computation of restricted structure controllers that are simple to implement. * Considers time-varying control and signal processing problems. * Addresses the control of non-linear processes using both multiple model concepts and new optimal control solutions. Robust Industrial Control Systems: Optimal Design Approach for Polynomial Systems is essential reading for professional engineers requiring an introduction to optimal control theory and insights into its use in the design of real industrial processes. Students and researchers in the field will also find it an excellent reference tool. |
| tsukamoto fuzzy model: Hydrological Data Driven Modelling Renji Remesan, Jimson Mathew, 2014-11-03 This book explores a new realm in data-based modeling with applications to hydrology. Pursuing a case study approach, it presents a rigorous evaluation of state-of-the-art input selection methods on the basis of detailed and comprehensive experimentation and comparative studies that employ emerging hybrid techniques for modeling and analysis. Advanced computing offers a range of new options for hydrologic modeling with the help of mathematical and data-based approaches like wavelets, neural networks, fuzzy logic, and support vector machines. Recently machine learning/artificial intelligence techniques have come to be used for time series modeling. However, though initial studies have shown this approach to be effective, there are still concerns about their accuracy and ability to make predictions on a selected input space. |
| tsukamoto fuzzy model: Energy Management—Collective and Computational Intelligence with Theory and Applications Cengiz Kahraman, Gülgün Kayakutlu, 2018-03-21 This book presents a selection of recently developed collective and computational intelligence techniques, which it subsequently applies to energy management problems ranging from performance analysis to economic analysis, and from strategic analysis to operational analysis, with didactic numerical examples. As a form of intelligence emerging from the collaboration and competition of individuals, collective and computational intelligence addresses new methodological, theoretical, and practical aspects of complex energy management problems. The book offers an excellent reference guide for practitioners, researchers, lecturers and postgraduate students pursuing research on intelligence in energy management. The contributing authors are recognized researchers in the energy research field. |
| tsukamoto fuzzy model: Rock Fragmentation by Blasting Jose A. Sanchidrian, 2009-08-20 This volume contains the papers presented at the 9th International Symposium on Rock Fragmentation by Blasting, held in Granada, Spain, 13-17 August 2009. A state-of-the-art collection of articles on developments in rock blasting and explosives engineering, with contributions on rock characterization, explosives and initiation systems, blast design and monitoring, fragmentation assessment, numerical modeling, vibrations from blasting, environmental and economical aspects of rock blasting, and more. Containing unique knowledge, case studies, ideas and insights, this volume is must-have literature for researchers and practitioners in the field of explosives and blasting. |
| tsukamoto fuzzy model: Handbook of Research on Advanced Computational Techniques for Simulation-Based Engineering Samui, Pijush, 2015-11-30 Recent developments in information processing systems have driven the advancement of computational methods in the engineering realm. New models and simulations enable better solutions for problem-solving and overall process improvement. The Handbook of Research on Advanced Computational Techniques for Simulation-Based Engineering is an authoritative reference work representing the latest scholarly research on the application of computational models to improve the quality of engineering design. Featuring extensive coverage on a range of topics from various engineering disciplines, including, but not limited to, soft computing methods, comparative studies, and hybrid approaches, this book is a comprehensive reference source for students, professional engineers, and researchers interested in the application of computational methods for engineering design. |
| tsukamoto fuzzy model: Artificial Intelligence and Natural Algorithms Rijwan Khan, 2022-09-23 This book informs the reader about applications of Artificial Intelligence (AI) and nature-inspired algorithms in different situations. Each chapter in this book is written by topic experts on AI, nature-inspired algorithms and data science. The basic concepts relevant to these topics are explained, including evolutionary computing (EC), artificial neural networks (ANN), swarm intelligence (SI), and fuzzy systems (FS). Additionally, the book also covers optimization algorithms for data analysis. The contents include algorithms that can be used in systems designed for plant science research, load balancing, environmental analysis and healthcare. The goal of the book is to equip the reader - students and data analysts - with the information needed to apply basic AI algorithms to resolve actual problems encountered in a professional environment. |
| tsukamoto fuzzy model: Neuro-fuzzy and Soft Computing Jyh-Shing Roger Jang, Chuen-Tsai Sun, Eiji Mizutani, 1997 Neuro-Fuzzy and Soft Computing provides the first comprehensive treatment of the constituent methodologies underlying neuro-fuzzy and soft computing, an evolving branch of computational intelligence. The constituent methodologies include fuzzy set theory, neural networks, data clustering techniques, and several stochastic optimization methods that do not require gradient information. In particular, the authors put equal emphasis on theoretical aspects of covered methodologies, as well as empirical observations and verifications of various applications in practice. The book is well suited for use as a text for courses on computational intelligence and as a single reference source for this emerging field. To help readers understand the material the presentation includes more than 50 examples, more than 150 exercises, over 300 illustrations, and more than 150 Matlab scripts. In addition, Matlab is utilized to visualize the processes of fuzzy reasoning, neural-network learning, neuro-fuzzy integration and training, and gradient-free optimization (such as genetic algorithms, simulated annealing, random search, and downhill Simplex method). The presentation also makes use of SIMULINK for neuro-fuzzy control system simulations. All Matlab scripts used in the book are available on the free companion software disk that may be ordered by using the enclosed reply card. The book also contains an Internet Resource Page to point the reader to on-line neuro-fuzzy and soft computing home pages, publications, public-domain software, research institutes, news groups, etc. All the HTTP and FTP addresses are available as a bookmark file on the companion software disk. |
| tsukamoto fuzzy model: Handbook of Research on Trends and Digital Advances in Engineering Geology Ceryan, Nurcihan, 2017-07-12 Engineering geologists face the task of addressing geological factors that can affect planning with little time and with few resources. A solution is using the right tools to save time searching for answers and devote attention to making critical engineering decisions. The Handbook of Research on Trends and Digital Advances in Engineering Geology is an essential reference source for the latest research on new trends, technology, and computational methods that can model engineering phenomena automatically. Featuring exhaustive coverage on a broad range of topics and perspectives such as acoustic energy, landslide mapping, and natural hazards, this publication is ideally designed for academic scientists, industry and applied researchers, and policy and decision makers seeking current research on new tools to aid in timely decision-making of critical engineering situations. |
| tsukamoto fuzzy model: Computational Intelligence Nazmul Siddique, Hojjat Adeli, 2013-05-06 Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing presents an introduction to some of the cutting edge technological paradigms under the umbrella of computational intelligence. Computational intelligence schemes are investigated with the development of a suitable framework for fuzzy logic, neural networks and evolutionary computing, neuro-fuzzy systems, evolutionary-fuzzy systems and evolutionary neural systems. Applications to linear and non-linear systems are discussed with examples. Key features: Covers all the aspects of fuzzy, neural and evolutionary approaches with worked out examples, MATLAB® exercises and applications in each chapter Presents the synergies of technologies of computational intelligence such as evolutionary fuzzy neural fuzzy and evolutionary neural systems Considers real world problems in the domain of systems modelling, control and optimization Contains a foreword written by Lotfi Zadeh Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing is an ideal text for final year undergraduate, postgraduate and research students in electrical, control, computer, industrial and manufacturing engineering. |
| tsukamoto fuzzy model: Controlling Epidemics With Mathematical and Machine Learning Models Varghese, Abraham, Lacap, Jr., Eduardo M., Sajath, Ibrahim, Kumar, M. Kamal, Kolamban, Shajidmon, 2022-10-21 Communicable diseases have been an important part of human history. Epidemics afflicted populations, causing many deaths before gradually fading away and emerging again years after. Epidemics of infectious diseases are occurring more often, and spreading faster and further than ever, in many different regions of the world. The scientific community, in addition to its accelerated efforts to develop an effective treatment and vaccination, is also playing an important role in advising policymakers on possible non-pharmacological approaches to limit the catastrophic impact of epidemics using mathematical and machine learning models. Controlling Epidemics With Mathematical and Machine Learning Models provides mathematical and machine learning models for epidemical diseases, with special attention given to the COVID-19 pandemic. It gives mathematical proof of the stability and size of diseases. Covering topics such as compartmental models, reproduction number, and SIR model simulation, this premier reference source is an essential resource for statisticians, government officials, health professionals, epidemiologists, sociologists, students and educators of higher education, librarians, researchers, and academicians. |
| tsukamoto fuzzy model: Intelligent Internet of Things for Healthcare and Industry Uttam Ghosh, Chinmay Chakraborty, Lalit Garg, Gautam Srivastava, 2022-02-12 This book promotes and facilitates exchanges of research knowledge and findings across different disciplines on the design and investigation of machine learning-based data analytics of IoT infrastructures. This book is focused on the emerging trends, strategies, and applications of IoT in both healthcare and industry data analytics perspectives. The data analytics discussed are relevant for healthcare and industry to meet many technical challenges and issues that need to be addressed to realize this potential. The IoT discussed helps to design and develop the intelligent medical and industry solutions assisted by data analytics and machine learning. At the end of every chapter readers are encouraged to check their understanding by means of brainstorming summary, discussion, exercises and solutions. |
| tsukamoto fuzzy model: Intelligent and Fuzzy Techniques in Aviation 4.0 Cengiz Kahraman, Serhat Aydın, 2021-08-26 This book offers a comprehensive reference guide for the theory and practice of intelligent and fuzzy techniques in Aviation 4.0. It provides readers with the necessary intelligent and fuzzy tools for Aviation 4.0 when incomplete, vague, and imprecise information or insufficient data exist in hand, where classical modeling approaches cannot be applied. The respective chapters, written by prominent researchers, explain a wealth of both basic and advanced concepts including baggage services, catering services, check-in and boarding services, maintenance and cargo management, security, etc. To foster reader comprehension, all chapters include relevant numerical examples or case studies. Taken together, they form an excellent reference guide for researchers, lecturers, and postgraduate students pursuing research on Aviation 4.0. Moreover, by extending all the main aspects of Aviation 4.0 to its intelligent and fuzzy counterparts, the book presents a dynamic snapshot of the field that is expected to stimulate new directions, ideas, and developments. |
| tsukamoto fuzzy model: Handbook of Alkali-Activated Cements, Mortars and Concretes F. Pacheco-Torgal, Joao Labrincha, C Leonelli, A Palomo, P Chindaprasit, 2014-11-20 This book provides an updated state-of-the-art review on new developments in alkali-activation. The main binder of concrete, Portland cement, represents almost 80% of the total CO2 emissions of concrete which are about 6 to 7% of the Planet's total CO2 emissions. This is particularly serious in the current context of climate change and it could get even worse because the demand for Portland cement is expected to increase by almost 200% by 2050 from 2010 levels, reaching 6000 million tons/year. Alkali-activated binders represent an alternative to Portland cement having higher durability and a lower CO2 footprint. - Reviews the chemistry, mix design, manufacture and properties of alkali-activated cement-based concrete binders - Considers performance in adverse environmental conditions. - Offers equal emphasis on the science behind the technology and its use in civil engineering. |
| tsukamoto fuzzy model: Soft Computing Samir Roy, Udit Chakraborty, 2013 Soft computing is a branch of computer science that deals with a family of methods that imitate human intelligence. This is done with the goal of creating tools that will contain some human-like capabilities (such as learning, reasoning and decision-making). This book covers the entire gamut of soft computing, including fuzzy logic, rough sets, artificial neural networks, and various evolutionary algorithms. It offers a learner-centric approach where each new concept is introduced with carefully designed examples/instances to train the learner. |
| tsukamoto fuzzy model: Applications of Image Processing and Soft Computing Systems in Agriculture Razmjooy, Navid, Estrela, Vania Vieira, 2019-02-22 The variety and abundance of qualitative characteristics of agricultural products have been the main reasons for the development of different types of non-destructive methods (NDTs). Quality control of these products is one of the most important tasks in manufacturing processes. The use of control and automation has become more widespread, and new approaches provide opportunities for production competition through new technologies. Applications of Image Processing and Soft Computing Systems in Agriculture examines applications of artificial intelligence in agriculture and the main uses of shape analysis on agricultural products such as relationships between form and genetics, adaptation, product characteristics, and product sorting. Additionally, it provides insights developed through computer vision techniques. Highlighting such topics as deep learning, agribusiness, and augmented reality, it is designed for academicians, researchers, agricultural practitioners, and industry professionals. |
| tsukamoto fuzzy model: Earth System Sciences Virendra Krishna Verma, 2009 Contributed articles; volume to commemorate the 75th birth anniversary of Virendra Krishna Verma, b. 1934, Indian geologist. |
| tsukamoto fuzzy model: Soft Computing for Control of Non-Linear Dynamical Systems Oscar Castillo, Patricia Melin, 2012-12-06 This book presents a unified view of modelling, simulation, and control of non linear dynamical systems using soft computing techniques and fractal theory. Our particular point of view is that modelling, simulation, and control are problems that cannot be considered apart, because they are intrinsically related in real world applications. Control of non-linear dynamical systems cannot be achieved if we don't have the appropriate model for the system. On the other hand, we know that complex non-linear dynamical systems can exhibit a wide range of dynamic behaviors ( ranging from simple periodic orbits to chaotic strange attractors), so the problem of simulation and behavior identification is a very important one. Also, we want to automate each of these tasks because in this way it is more easy to solve a particular problem. A real world problem may require that we use modelling, simulation, and control, to achieve the desired level of performance needed for the particular application. |
| tsukamoto fuzzy model: Fuzzy Logic Theory And Applications: Part I And Part Ii Lotfi A Zadeh, Rafik Aziz Aliev, 2018-12-04 Nowadays, voluminous textbooks and monographs in fuzzy logic are devoted only to separate or some combination of separate facets of fuzzy logic. There is a lack of a single book that presents a comprehensive and self-contained theory of fuzzy logic and its applications.Written by world renowned authors, Lofti Zadeh, also known as the Father of Fuzzy Logic, and Rafik Aliev, who are pioneers in fuzzy logic and fuzzy sets, this unique compendium includes all the principal facets of fuzzy logic such as logical, fuzzy-set-theoretic, epistemic and relational. Theoretical problems are prominently illustrated and illuminated by numerous carefully worked-out and thought-through examples.This invaluable volume will be a useful reference guide for academics, practitioners, graduates and undergraduates in fuzzy logic and its applications. |
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Shop and find deals from your local store in our Weekly Ad. Updated each week, find sales on grocery, meat and seafood, produce, cleaning supplies, beauty, baby products and more.
Sign-Up & Save with Our Digital Weekly Ad - Kroger
Weekly Ad is going full digital! Sign-up for free and start saving with exclusive deals, personalized coupons and more.
Weekly Ad Info - Kroger
Shop Items Use your list to find offers in-store, or buy them on the website or app. Save Money Weekly ad savings are automatically applied at checkout, both in-store and online. More …
Sales, Deals & Promotions - Kroger
Find the different ways that you can save while shopping online. Check out the Weekly Ad, digital coupons and deals, and order online for pickup or delivery.
Weekly Ad FAQs - Kroger
Learn how to check weekly ad specials online, creating an account and setting a preferred store to view deals.
Kroger Huntsville Grocery Pickup Huntsville, TX | 223 Interstate 45 S
Order now for grocery pickup in Huntsville, TX at Kroger. Online grocery pickup lets you order groceries online and pick them up at your nearest store. Find a grocery store near you.
weekly kroger ad
Home Search: weekly kroger ad All Filters Price Range Savings Brands Ways to Shop Flavor Departments Dietary Preferences SNAP EBT Eligible
Weekly Digital Deals - Kroger
Save all week long on your favorite products with our weekly digital deals. Find out more about these deals now!
3205 N University Dr Nacogdoches, TX - Kroger
University Park Store hours are currently unavailable. Please call the store for more information. Open Until 12 AM 3205 N University Dr Nacogdoches, TX 75965 (External site) (Opens in a new …
Kroger Park Ave Grocery Pickup Paducah, KY | 3141 Park Ave
Order now for grocery pickup in Paducah, KY at Kroger. Online grocery pickup lets you order groceries online and pick them up at your nearest store. Find a grocery store near you.
