Fuzzy Neural Networks for Real Time Control Applications

Fuzzy Neural Networks for Real Time Control Applications
Author: Erdal Kayacan,Mojtaba Ahmadieh Khanesar
Publsiher: Butterworth-Heinemann
Total Pages: 264
Release: 2015-10-07
Genre: Computers
ISBN: 9780128027035

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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

Neural Network Applications in Control

Neural Network Applications in Control
Author: George William Irwin,K. Warwick,Kenneth J. Hunt
Publsiher: IET
Total Pages: 320
Release: 1995
Genre: Computers
ISBN: 0852968523

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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.

Fuzzy neural Control

Fuzzy neural Control
Author: Junhong Nie,D. A. Linkens
Publsiher: Prentice Hall PTR
Total Pages: 262
Release: 1995
Genre: Fuzzy sistemler
ISBN: UOM:39015033961247

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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 Fuzzy Control Systems with Structure and Parameter Learning

Neural Fuzzy Control Systems with Structure and Parameter Learning
Author: C. T. Lin,Ching Tai Lin
Publsiher: World Scientific
Total Pages: 150
Release: 1994
Genre: Computers
ISBN: 9810216130

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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.

Design of Interpretable Fuzzy Systems

Design of Interpretable Fuzzy Systems
Author: Krzysztof Cpałka
Publsiher: Springer
Total Pages: 196
Release: 2017-01-31
Genre: Technology & Engineering
ISBN: 9783319528816

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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.

Networked Control Systems

Networked Control Systems
Author: Fei-Yue Wang,Derong Liu
Publsiher: Springer Science & Business Media
Total Pages: 352
Release: 2008-06-17
Genre: Computers
ISBN: 9781848002159

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Networked control systems (NCS) confer advantages of cost reduction, system diagnosis and flexibility, minimizing wiring and simplifying the addition and replacement of individual elements; efficient data sharing makes taking globally intelligent control decisions easier with NCS. The applications of NCS range from the large scale of factory automation and plant monitoring to the smaller networks of computers in modern cars, places and autonomous robots. Networked Control Systems presents recent results in stability and robustness analysis and new developments related to networked fuzzy and optimal control. Many chapters contain case-studies, experimental, simulation or other application-related work showing how the theories put forward can be implemented. The state-of-the art research reported in this volume by an international team of contributors makes it an essential reference for researchers and postgraduate students in control, electrical, computer and mechanical engineering and computer science.

Artificial Intelligence in Real Time Control 1994

Artificial Intelligence in Real Time Control 1994
Author: A. Crespo
Publsiher: Elsevier
Total Pages: 390
Release: 2014-06-28
Genre: Technology & Engineering
ISBN: 9781483296937

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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.

New Advances at the Intersection of Brain Inspired Learning and Deep Learning in Autonomous Vehicles and Robotics

New Advances at the Intersection of Brain Inspired Learning and Deep Learning in Autonomous Vehicles and Robotics
Author: Guang Chen,Pascual Campoy,Changhong Fu,Caixia Cai
Publsiher: Frontiers Media SA
Total Pages: 129
Release: 2020-09-02
Genre: Medical
ISBN: 9782889639717

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This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact.