Fuzzy Neural Networks for Real Time Control Applications

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

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  • Author : Erdal Kayacan
  • Publisher : Butterworth-Heinemann
  • Pages : 264 pages
  • ISBN : 0128027037
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKFuzzy Neural Networks for Real Time Control Applications

Fuzzy Neural Networks for Real Time Control Applications

Fuzzy Neural Networks for Real Time Control Applications
  • Author : Erdal Kayacan,Mojtaba Ahmadieh Khanesar
  • Publisher : Butterworth-Heinemann
  • Release : 07 October 2015
GET THIS BOOKFuzzy Neural Networks for Real Time Control Applications

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

Fuzzy Neural Networks for Real Time Control Applications

Fuzzy Neural Networks for Real Time Control Applications
  • Author : Erdal Kayacan,Mojtaba Ahmadieh Khanesar
  • Publisher : Butterworth-Heinemann
  • Release : 17 September 2015
GET THIS BOOKFuzzy Neural Networks for Real Time Control Applications

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

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
  • Publisher : World Scientific
  • Release : 27 February 1994
GET THIS BOOKNeural Fuzzy Control Systems with Structure and Parameter Learning

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

Fuzzy-neural Control

Fuzzy-neural Control
  • Author : Junhong Nie,D. A. Linkens
  • Publisher : Prentice Hall PTR
  • Release : 27 February 1995
GET THIS BOOKFuzzy-neural Control

Shows how Fuzzy Logic and Neural Networks can be intgrated into a Model Reference Control context for real-time control of multivariable systems. It provides a unified architecture which accommodates several popular learning/reasoning paradigms, including Counter Propagation Networks, Radial Basis Functions and CMAC a fuzzy context. Unified treatment of fuzzy-algorithm-based and neural network based control systems. Introduces new fuzzy-nueral controller structures. Demonstrates the feasibility of proposed approach by showing applications. Graduate students of Neural Networks, Intellegent Control and fuzzy matters

Fuzzy Neural Intelligent Systems

Fuzzy Neural Intelligent Systems
  • Author : Hongxing Li,C.L. Philip Chen,Han-Pang Huang
  • Publisher : CRC Press
  • Release : 03 October 2018
GET THIS BOOKFuzzy Neural Intelligent Systems

Although fuzzy systems and neural networks are central to the field of soft computing, most research work has focused on the development of the theories, algorithms, and designs of systems for specific applications. There has been little theoretical support for fuzzy neural systems, especially their mathematical foundations. Fuzzy Neural Intelligent Systems fills this gap. It develops a mathematical basis for fuzzy neural networks, offers a better way of combining fuzzy logic systems with neural networks, and explores some of their

Neural Network Applications in Control

Neural Network Applications in Control
  • Author : Institution of Electrical Engineers
  • Publisher : IET
  • Release : 27 February 1995
GET THIS BOOKNeural Network Applications in Control

Introducing a wide variety of network types, including Kohenen nets, n-tuple nets and radial basis function networks as well as the more useful multilayer perception back-propagation networks, this book aims to give a detailed appreciation of the use of neural nets in these applications.

Safety, Reliability, and Applications of Emerging Intelligent Control Technologies

Safety, Reliability, and Applications of Emerging Intelligent Control Technologies
  • Author : International Federation of Automatic Control
  • Publisher : Pergamon
  • Release : 27 February 1995
GET THIS BOOKSafety, Reliability, and Applications of Emerging Intelligent Control Technologies

Paperback. Increasingly, over the last few years, intelligent controllers have been incorporated into control systems. Presently, the numbers and types of intelligent controllers that contain variations of fuzzy logic, neural network, genetic algorithms or some other forms of knowledge based reasoning technology are dramatically rising. However, considering the stability of the system, when such controllers are included it is difficult to analyse and predict system behaviour under unexpected conditions. Leading researchers and industrial practitioners were able to discuss and evaluate

Artificial Intelligence in Real-Time Control 1994

Artificial Intelligence in Real-Time Control 1994
  • Author : A. Crespo
  • Publisher : Elsevier
  • Release : 28 June 2014
GET THIS BOOKArtificial Intelligence in Real-Time Control 1994

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

Intelligent Components and Instruments for Control Applications 2003 (SICICA 2003)

Intelligent Components and Instruments for Control Applications 2003 (SICICA 2003)
  • Author : L. Almeida,Luis B. Almeida,S. Boverie
  • Publisher : Elsevier
  • Release : 27 February 2021
GET THIS BOOKIntelligent Components and Instruments for Control Applications 2003 (SICICA 2003)

A Proceedings volume from the IFAC Symposium on Intelligent Components and Instruments for Control Applications, Portugal, 2003. Provides an overview of the theory and applications and presents an exchange of experiences on recent advances in this field.

Soft Computing as Transdisciplinary Science and Technology

Soft Computing as Transdisciplinary Science and Technology
  • Author : Ajith Abraham,Yasuhiko Dote,Takeshi Furuhashi,Mario Köppen,Azuma Ohuchi,Yukio Ohsawa
  • Publisher : Springer Science & Business Media
  • Release : 14 December 2007
GET THIS BOOKSoft Computing as Transdisciplinary Science and Technology

This book presents the proceedings of the Fourth International Workshop on Soft Computing as Transdisciplinary Science and Technology (WSTST '05), May 25-27, 2005, Muroran, Japan. It brings together the original work of international soft computing/computational intelligence researchers, developers, practitioners, and users. This proceedings provide contributions to all areas of soft computing including intelligent hybrid systems, agent-based systems, intelligent data mining, decision support systems, cognitive and reactive distributed artificial intelligence (AI), internet modelling, human interface, and applications in science and technology.

Artificial Neural Networks for Engineering Applications

Artificial Neural Networks for Engineering Applications
  • Author : Alma Y. Alanis,Nancy Arana-Daniel,Carlos Lopez-Franco
  • Publisher : Academic Press
  • Release : 15 March 2019
GET THIS BOOKArtificial Neural Networks for Engineering Applications

Artificial Neural Networks for Engineering Applications presents current trends for the solution of complex engineering problems that cannot be solved through conventional methods. The proposed methodologies can be applied to modeling, pattern recognition, classification, forecasting, estimation, and more. Readers will find different methodologies to solve various problems, including complex nonlinear systems, cellular computational networks, waste water treatment, attack detection on cyber-physical systems, control of UAVs, biomechanical and biomedical systems, time series forecasting, biofuels, and more. Besides the real-time implementations, the

Artificial Intelligence in Real-Time Control 1992

Artificial Intelligence in Real-Time Control 1992
  • Author : M.G. Rodd,H.B. Verbruggen
  • Publisher : Elsevier
  • Release : 28 June 2014
GET THIS BOOKArtificial Intelligence in Real-Time Control 1992

The symposium had two main aims, to investigate the state-of-the-art in the application of artificial intelligence techniques in real-time control, and to bring together control system specialists, artificial intelligence specialists and end-users. Many professional engineers working in industry feel that the gap between theory and practice in applying control and systems theory is widening, despite efforts to develop control algorithms. Papers presented at the meeting ranged from the theoretical aspects to the practical applications of artificial intelligence in real-time control.