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Type-2 Fuzzy Neural Networks and Their Applications

Author : Rafik Aziz Aliev

Publisher : Springer

Page : 203 pages

File Size : 17,56 MB

Release : 2014-09-08

Category : Computers

ISBN : 3319090720

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Book Description

This book deals with the theory, design principles, and application of hybrid intelligent systems using type-2 fuzzy sets in combination with other paradigms of Soft Computing technology such as Neuro-Computing and Evolutionary Computing. It provides a self-contained exposition of the foundation of type-2 fuzzy neural networks and presents a vast compendium of its applications to control, forecasting, decision making, system identification and other real problems. Type-2 Fuzzy Neural Networks and Their Applications is helpful for teachers and students of universities and colleges, for scientists and practitioners from various fields such as control, decision analysis, pattern recognition and similar fields.



Fuzzy Neural Networks for Real Time Control Applications

Author : Erdal Kayacan

Publisher : Butterworth-Heinemann

Page : 266 pages

File Size : 42,19 MB

Release : 2015-10-07

Category : Mathematics

ISBN : 0128027037

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Book Description

AN INDISPENSABLE RESOURCE FOR ALL THOSE WHO DESIGN AND IMPLEMENT TYPE-1 AND TYPE-2 FUZZY NEURAL NETWORKS IN REAL TIME SYSTEMS Delve into the type-2 fuzzy logic systems and become engrossed in the parameter update algorithms for type-1 and type-2 fuzzy neural networks and their stability analysis with this book! Not only does this book stand apart from others in its focus but also in its application-based presentation style. Prepared in a way that can be easily understood by those who are experienced and inexperienced in this field. Readers can benefit from the computer source codes for both identification and control purposes which are given at the end of the book. A clear and an in-depth examination has been made of all the necessary mathematical foundations, type-1 and type-2 fuzzy neural network structures and their learning algorithms as well as their stability analysis. You will find that each chapter is devoted to a different learning algorithm for the tuning of type-1 and type-2 fuzzy neural networks; some of which are: • Gradient descent • Levenberg-Marquardt • Extended Kalman filter In addition to the aforementioned conventional learning methods above, number of novel sliding mode control theory-based learning algorithms, which are simpler and have closed forms, and their stability analysis have been proposed. Furthermore, hybrid methods consisting of particle swarm optimization and sliding mode control theory-based algorithms have also been introduced. The potential readers of this book are expected to be the undergraduate and graduate students, engineers, mathematicians and computer scientists. Not only can this book be used as a reference source for a scientist who is interested in fuzzy neural networks and their real-time implementations but also as a course book of fuzzy neural networks or artificial intelligence in master or doctorate university studies. We hope that this book will serve its main purpose successfully. - Parameter update algorithms for type-1 and type-2 fuzzy neural networks and their stability analysis - Contains algorithms that are applicable to real time systems - Introduces fast and simple adaptation rules for type-1 and type-2 fuzzy neural networks - Number of case studies both in identification and control - Provides MATLAB® codes for some algorithms in the book



Type-2 Fuzzy Logic: Theory and Applications

Author : Oscar Castillo

Publisher : Springer Science & Business Media

Page : 252 pages

File Size : 31,29 MB

Release : 2008-02-20

Category : Mathematics

ISBN : 3540762833

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Book Description

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.



New Backpropagation Algorithm with Type-2 Fuzzy Weights for Neural Networks

Author : Fernando Gaxiola

Publisher : Springer

Page : 111 pages

File Size : 21,98 MB

Release : 2016-06-02

Category : Technology & Engineering

ISBN : 3319340875

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Book Description

In this book a neural network learning method with type-2 fuzzy weight adjustment is proposed. The mathematical analysis of the proposed learning method architecture and the adaptation of type-2 fuzzy weights are presented. The proposed method is based on research of recent methods that handle weight adaptation and especially fuzzy weights.The internal operation of the neuron is changed to work with two internal calculations for the activation function to obtain two results as outputs of the proposed method. Simulation results and a comparative study among monolithic neural networks, neural network with type-1 fuzzy weights and neural network with type-2 fuzzy weights are presented to illustrate the advantages of the proposed method.The proposed approach is based on recent methods that handle adaptation of weights using fuzzy logic of type-1 and type-2. The proposed approach is applied to a cases of prediction for the Mackey-Glass (for ô=17) and Dow-Jones time series, and recognition of person with iris biometric measure. In some experiments, noise was applied in different levels to the test data of the Mackey-Glass time series for showing that the type-2 fuzzy backpropagation approach obtains better behavior and tolerance to noise than the other methods.The optimization algorithms that were used are the genetic algorithm and the particle swarm optimization algorithm and the purpose of applying these methods was to find the optimal type-2 fuzzy inference systems for the neural network with type-2 fuzzy weights that permit to obtain the lowest prediction error.



Modular Neural Networks and Type-2 Fuzzy Systems for Pattern Recognition

Author : Patricia Melin

Publisher : Springer

Page : 216 pages

File Size : 13,6 MB

Release : 2011-10-25

Category : Technology & Engineering

ISBN : 3642241395

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Book Description

This book describes hybrid intelligent systems using type-2 fuzzy logic and modular neural networks for pattern recognition applications. Hybrid intelligent systems combine several intelligent computing paradigms, including fuzzy logic, neural networks, and bio-inspired optimization algorithms, which can be used to produce powerful pattern recognition systems. Type-2 fuzzy logic is an extension of traditional type-1 fuzzy logic that enables managing higher levels of uncertainty in complex real world problems, which are of particular importance in the area of pattern recognition. The book is organized in three main parts, each containing a group of chapters built around a similar subject. The first part consists of chapters with the main theme of theory and design algorithms, which are basically chapters that propose new models and concepts, which are the basis for achieving intelligent pattern recognition. The second part contains chapters with the main theme of using type-2 fuzzy models and modular neural networks with the aim of designing intelligent systems for complex pattern recognition problems, including iris, ear, face and voice recognition. The third part contains chapters with the theme of evolutionary optimization of type-2 fuzzy systems and modular neural networks in the area of intelligent pattern recognition, which includes the application of genetic algorithms for obtaining optimal type-2 fuzzy integration systems and ideal neural network architectures for solving problems in this area.



Recent Advances in Interval Type-2 Fuzzy Systems

Author : Oscar Castillo

Publisher : Springer Science & Business Media

Page : 93 pages

File Size : 27,55 MB

Release : 2012-04-23

Category : Technology & Engineering

ISBN : 3642289568

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Book Description

This book reviews current state of the art methods for building intelligent systems using type-2 fuzzy logic and bio-inspired optimization techniques. Combining type-2 fuzzy logic with optimization algorithms, powerful hybrid intelligent systems have been built using the advantages that each technique offers. This book is intended to be a reference for scientists and engineers interested in applying type-2 fuzzy logic for solving problems in pattern recognition, intelligent control, intelligent manufacturing, robotics and automation. This book can also be used as a reference for graduate courses like the following: soft computing, intelligent pattern recognition, computer vision, applied artificial intelligence, and similar ones. We consider that this book can also be used to get novel ideas for new lines of re-search, or to continue the lines of research proposed by the authors.



NEURAL NETWORKS, FUZZY SYSTEMS AND EVOLUTIONARY ALGORITHMS : SYNTHESIS AND APPLICATIONS

Author : S. RAJASEKARAN

Publisher : PHI Learning Pvt. Ltd.

Page : 574 pages

File Size : 36,97 MB

Release : 2017-05-01

Category : Computers

ISBN : 812035334X

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Book Description

The second edition of this book provides a comprehensive introduction to a consortium of technologies underlying soft computing, an evolving branch of computational intelligence, which in recent years, has turned synonymous to it. The constituent technologies discussed comprise neural network (NN), fuzzy system (FS), evolutionary algorithm (EA), and a number of hybrid systems, which include classes such as neuro-fuzzy, evolutionary-fuzzy, and neuro-evolutionary systems. The hybridization of the technologies is demonstrated on architectures such as fuzzy backpropagation network (NN-FS hybrid), genetic algorithm-based backpropagation network (NN-EA hybrid), simplified fuzzy ARTMAP (NN-FS hybrid), fuzzy associative memory (NN-FS hybrid), fuzzy logic controlled genetic algorithm (EA-FS hybrid) and evolutionary extreme learning machine (NN-EA hybrid) Every architecture has been discussed in detail through illustrative examples and applications. The algorithms have been presented in pseudo-code with a step-by-step illustration of the same in problems. The applications, demonstrative of the potential of the architectures, have been chosen from diverse disciplines of science and engineering. This book, with a wealth of information that is clearly presented and illustrated by many examples and applications, is designed for use as a text for the courses in soft computing at both the senior undergraduate and first-year postgraduate levels of computer science and engineering. It should also be of interest to researchers and technologists desirous of applying soft computing technologies to their respective fields of work.



Advanced Computing in Industrial Mathematics

Author : Krassimir Georgiev

Publisher : Springer

Page : 224 pages

File Size : 26,1 MB

Release : 2017-10-25

Category : Technology & Engineering

ISBN : 3319655302

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Book Description

This book presents recent research on Advanced Computing in Industrial Mathematics, which is one of the most prominent interdisciplinary areas, bringing together mathematics, computer science, scientific computations, engineering, physics, chemistry, medicine, etc. Further, the book presents the major tools used in Industrial Mathematics, which are based on mathematical models, and the corresponding computer codes, which are used to perform virtual experiments to obtain new data or to better understand previous experimental findings. The book gathers the peer-reviewed papers presented at the 11th Annual Meeting of the Bulgarian Section of SIAM (BGSIAM), from December 20 to 22, 2016 in Sofia, Bulgaria.



Recent Trends on Type-2 Fuzzy Logic Systems: Theory, Methodology and Applications

Author : Oscar Castillo

Publisher : Springer Nature

Page : 273 pages

File Size : 41,14 MB

Release : 2023-03-31

Category : Technology & Engineering

ISBN : 3031263324

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Book Description

This book covers the introduction, theory, development and applications of type-2 fuzzy logic systems, which represent the current state-of-the-art in various domains such as control applications, power plants, health care, image processing, mathematical applications etc. The book is also rich in discussing different applications in order to give the researchers a flavor of how type-2 fuzzy logic is designed for different types of problems. Type-2 fuzzy logic systems are now used extensively in engineering applications for many purposes. In simple language, this book covers the practical use of type-2 fuzzy logic and its optimization through different training methods. Furthermore, this book maintains the relationship between mathematics and practical implementations in the real world. This book chapter also contains the proper comparisons with available literature work. It shows that the presented enhanced techniques have better results. This book would serve as a handy reference guide for a variety of readers, primarily targeting research scholars, undergraduate and postgraduate researchers and practicing engineers working in Type-2 fuzzy logic systems and their applications.



Fuzzy Modelling

Author : Witold Pedrycz

Publisher : Springer Science & Business Media

Page : 399 pages

File Size : 29,58 MB

Release : 2012-12-06

Category : Mathematics

ISBN : 1461313651

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Book Description

Fuzzy Modelling: Paradigms and Practice provides an up-to-date and authoritative compendium of fuzzy models, identification algorithms and applications. Chapters in this book have been written by the leading scholars and researchers in their respective subject areas. Several of these chapters include both theoretical material and applications. The editor of this volume has organized and edited the chapters into a coherent and uniform framework. The objective of this book is to provide researchers and practitioners involved in the development of models for complex systems with an understanding of fuzzy modelling, and an appreciation of what makes these models unique. The chapters are organized into three major parts covering relational models, fuzzy neural networks and rule-based models. The material on relational models includes theory along with a large number of implemented case studies, including some on speech recognition, prediction, and ecological systems. The part on fuzzy neural networks covers some fundamentals, such as neurocomputing, fuzzy neurocomputing, etc., identifies the nature of the relationship that exists between fuzzy systems and neural networks, and includes extensive coverage of their architectures. The last part addresses the main design principles governing the development of rule-based models. Fuzzy Modelling: Paradigms and Practice provides a wealth of specific fuzzy modelling paradigms, algorithms and tools used in systems modelling. Also included is a panoply of case studies from various computer, engineering and science disciplines. This should be a primary reference work for researchers and practitioners developing models of complex systems.