Fuzzy Neural Networks for Real Time Control Applications

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Author :
Publisher : Butterworth-Heinemann
ISBN 13 : 0128027037
Total Pages : 264 pages
Book Rating : 4.35/5 ( download)

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Book Synopsis Fuzzy Neural Networks for Real Time Control Applications by : Erdal Kayacan

Download or read book Fuzzy Neural Networks for Real Time Control Applications written by Erdal Kayacan and published by Butterworth-Heinemann. This book was released on 2015-10-07 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Fuzzy-neural Control

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Author :
Publisher : Prentice Hall PTR
ISBN 13 :
Total Pages : 262 pages
Book Rating : 4.47/5 ( download)

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Book Synopsis Fuzzy-neural Control by : Junhong Nie

Download or read book Fuzzy-neural Control written by Junhong Nie and published by Prentice Hall PTR. This book was released on 1995 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: Illustrating how fuzzy logic and neural networks can be integrated into a model reference control context for real-time control of multivariable systems, this book provides an architecture which accommodates several popular learning/reasoning paradigms.

Neural Network Applications in Control

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Publisher : IET
ISBN 13 : 9780852968529
Total Pages : 320 pages
Book Rating : 4.23/5 ( download)

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Book Synopsis Neural Network Applications in Control by : George William Irwin

Download or read book Neural Network Applications in Control written by George William Irwin and published by IET. This book was released on 1995 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: The aim is to present an introduction to, and an overview of, the present state of neural network research and development, with an emphasis on control systems application studies. The book is useful to a range of levels of reader. The earlier chapters introduce the more popular networks and the fundamental control principles, these are followed by a series of application studies, most of which are industrially based, and the book concludes with a consideration of some recent research.

Artificial Intelligence in Real-time Control

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Publisher :
ISBN 13 :
Total Pages : 410 pages
Book Rating : 4.15/5 ( download)

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Book Synopsis Artificial Intelligence in Real-time Control by :

Download or read book Artificial Intelligence in Real-time Control written by and published by . This book was released on 1994 with total page 410 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Neural Fuzzy Control Systems with Structure and Parameter Learning

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Publisher : World Scientific Publishing Company
ISBN 13 : 9813104708
Total Pages : 144 pages
Book Rating : 4.09/5 ( download)

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Book Synopsis Neural Fuzzy Control Systems with Structure and Parameter Learning by : Chin-Teng Lin

Download or read book Neural Fuzzy Control Systems with Structure and Parameter Learning written by Chin-Teng Lin and published by World Scientific Publishing Company. This book was released on 1994-02-08 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt: A general neural-network-based connectionist model, called Fuzzy Neural Network (FNN), is proposed in this book for the realization of a fuzzy logic control and decision system. The FNN is a feedforward multi-layered network which integrates the basic elements and functions of a traditional fuzzy logic controller into a connectionist structure which has distributed learning abilities. In order to set up this proposed FNN, the author recommends two complementary structure/parameter learning algorithms: a two-phase hybrid learning algorithm and an on-line supervised structure/parameter learning algorithm. Both of these learning algorithms require exact supervised training data for learning. In some real-time applications, exact training data may be expensive or even impossible to get. To solve this reinforcement learning problem for real-world applications, a Reinforcement Fuzzy Neural Network (RFNN) is further proposed. Computer simulation examples are presented to illustrate the performance and applicability of the proposed FNN, RFNN and their associated learning algorithms for various applications.

Neural Networks for Control

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Publisher : MIT Press
ISBN 13 : 9780262631617
Total Pages : 548 pages
Book Rating : 4.1X/5 ( download)

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Book Synopsis Neural Networks for Control by : W. Thomas Miller

Download or read book Neural Networks for Control written by W. Thomas Miller and published by MIT Press. This book was released on 1995 with total page 548 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural Networks for Control brings together examples of all the most important paradigms for the application of neural networks to robotics and control. Primarily concerned with engineering problems and approaches to their solution through neurocomputing systems, the book is divided into three sections: general principles, motion control, and applications domains (with evaluations of the possible applications by experts in the applications areas.) Special emphasis is placed on designs based on optimization or reinforcement, which will become increasingly important as researchers address more complex engineering challenges or real biological-control problems.A Bradford Book. Neural Network Modeling and Connectionism series

Artificial Intelligence in Real-Time Control 1994

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Author :
Publisher : Elsevier
ISBN 13 : 1483296938
Total Pages : 399 pages
Book Rating : 4.37/5 ( download)

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Book Synopsis Artificial Intelligence in Real-Time Control 1994 by : A. Crespo

Download or read book Artificial Intelligence in Real-Time Control 1994 written by A. Crespo and published by Elsevier. This book was released on 2014-06-28 with total page 399 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence is one of the new technologies that has contributed to the successful development and implementation of powerful and friendly control systems. These systems are more attractive to end-users shortening the gap between control theory applications. The IFAC Symposia on Artificial Intelligence in Real Time Control provides the forum to exchange ideas and results among the leading researchers and practitioners in the field. This publication brings together the papers presented at the latest in the series and provides a key evaluation of present and future developments of Artificial Intelligence in Real Time Control system technologies.

Design of Interpretable Fuzzy Systems

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Publisher : Springer
ISBN 13 : 3319528815
Total Pages : 196 pages
Book Rating : 4.16/5 ( download)

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Book Synopsis Design of Interpretable Fuzzy Systems by : Krzysztof Cpałka

Download or read book Design of Interpretable Fuzzy Systems written by Krzysztof Cpałka and published by Springer. This book was released on 2017-01-31 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book shows that the term “interpretability” goes far beyond the concept of readability of a fuzzy set and fuzzy rules. It focuses on novel and precise operators of aggregation, inference, and defuzzification leading to flexible Mamdani-type and logical-type systems that can achieve the required accuracy using a less complex rule base. The individual chapters describe various aspects of interpretability, including appropriate selection of the structure of a fuzzy system, focusing on improving the interpretability of fuzzy systems designed using both gradient-learning and evolutionary algorithms. It also demonstrates how to eliminate various system components, such as inputs, rules and fuzzy sets, whose reduction does not adversely affect system accuracy. It illustrates the performance of the developed algorithms and methods with commonly used benchmarks. The book provides valuable tools for possible applications in many fields including expert systems, automatic control and robotics.

Biologically Inspired Cognitive Architectures 2018

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Publisher : Springer
ISBN 13 : 331999316X
Total Pages : 362 pages
Book Rating : 4.64/5 ( download)

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Book Synopsis Biologically Inspired Cognitive Architectures 2018 by : Alexei V. Samsonovich

Download or read book Biologically Inspired Cognitive Architectures 2018 written by Alexei V. Samsonovich and published by Springer. This book was released on 2018-08-23 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book focuses on original approaches intended to support the development of biologically inspired cognitive architectures. It bridges together different disciplines, from classical artificial intelligence to linguistics, from neuro- and social sciences to design and creativity, among others. The chapters, based on contributions presented at the Ninth Annual Meeting of the BICA Society, held in on August 23-24, 2018, in Prague, Czech Republic, discuss emerging methods, theories and ideas towards the realization of general-purpose humanlike artificial intelligence or fostering a better understanding of the ways the human mind works. All in all, the book provides engineers, mathematicians, psychologists, computer scientists and other experts with a timely snapshot of recent research and a source of inspiration for future developments in the broadly intended areas of artificial intelligence and biological inspiration.

Discrete-Time Recurrent Neural Control

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Publisher : CRC Press
ISBN 13 : 1351377426
Total Pages : 205 pages
Book Rating : 4.23/5 ( download)

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Book Synopsis Discrete-Time Recurrent Neural Control by : Edgar N. Sanchez

Download or read book Discrete-Time Recurrent Neural Control written by Edgar N. Sanchez and published by CRC Press. This book was released on 2018-09-03 with total page 205 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book presents recent advances in the theory of neural control for discrete-time nonlinear systems with multiple inputs and multiple outputs. The simulation results that appear in each chapter include rigorous mathematical analyses, based on the Lyapunov approach, to establish its properties. The book contains two sections: the first focuses on the analyses of control techniques; the second is dedicated to illustrating results of real-time applications. It also provides solutions for the output trajectory tracking problem of unknown nonlinear systems based on sliding modes and inverse optimal control scheme. "This book on Discrete-time Recurrent Neural Control is unique in the literature, with new knowledge and information about the new technique of recurrent neural control especially for discrete-time systems. The book is well organized and clearly presented. It will be welcome by a wide range of researchers in science and engineering, especially graduate students and junior researchers who want to learn the new notion of recurrent neural control. I believe it will have a good market. It is an excellent book after all." — Guanrong Chen, City University of Hong Kong "This book includes very relevant topics, about neural control. In these days, Artificial Neural Networks have been recovering their relevance and well-stablished importance, this due to its great capacity to process big amounts of data. Artificial Neural Networks development always is related to technological advancements; therefore, it is not a surprise that now we are being witnesses of this new era in Artificial Neural Networks, however most of the developments in this research area only focuses on applicability of the proposed schemes. However, Edgar N. Sanchez author of this book does not lose focus and include both important applications as well as a deep theoretical analysis of Artificial Neural Networks to control discrete-time nonlinear systems. It is important to remark that first, the considered Artificial Neural Networks are development in discrete-time this simplify its implementation in real-time; secondly, the proposed applications ranging from modelling of unknown discrete-time on linear systems to control electrical machines with an emphasize to renewable energy systems. However, its applications are not limited to these kind of systems, due to their theoretical foundation it can be applicable to a large class of nonlinear systems. All of these is supported by the solid research done by the author." — Alma Y. Alanis, University of Guadalajara, Mexico "This book discusses in detail; how neural networks can be used for optimal as well as robust control design. Design of neural network controllers for real time applications such as induction motors, boost converters, inverted pendulum and doubly fed induction generators has also been carried out which gives the book an edge over other similar titles. This book will be an asset for the novice to the experienced ones." — Rajesh Joseph Abraham, Indian Institute of Space Science & Technology, Thiruvananthapuram, India