Neural Fuzzy Control Systems with Structure and Parameter Learning

Neural Fuzzy Control Systems with Structure and Parameter Learning PDF Author: C. T. Lin
Publisher: World Scientific
ISBN: 9789810216139
Category : Computers
Languages : en
Pages : 150

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Book Description
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 Fuzzy Control Systems with Structure and Parameter Learning

Neural Fuzzy Control Systems with Structure and Parameter Learning PDF Author: C. T. Lin
Publisher: World Scientific
ISBN: 9789810216139
Category : Computers
Languages : en
Pages : 150

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Book Description
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 Fuzzy Systems

Neural Fuzzy Systems PDF Author: Ching Tai Lin
Publisher: Prentice Hall
ISBN:
Category : Computers
Languages : en
Pages : 824

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Book Description
Neural Fuzzy Systems provides a comprehensive, up-to-date introduction to the basic theories of fuzzy systems and neural networks, as well as an exploration of how these two fields can be integrated to create Neural-Fuzzy Systems. It includes Matlab software, with a Neural Network Toolkit, and a Fuzzy System Toolkit.

Neuro-Fuzzy Control of Industrial Systems with Actuator Nonlinearities

Neuro-Fuzzy Control of Industrial Systems with Actuator Nonlinearities PDF Author: Frank L. Lewis
Publisher: SIAM
ISBN: 9780898717563
Category : Technology & Engineering
Languages : en
Pages : 258

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Book Description
Rigorous stability proofs are further verified by computer simulations, and appendices contain the computer code needed to build intelligent controllers for real-time applications. Neural networks capture the parallel processing and learning capabilities of biological nervous systems, and fuzzy logic captures the decision-making capabilities of human linguistics and cognitive systems.

Neuro-Fuzzy Architectures and Hybrid Learning

Neuro-Fuzzy Architectures and Hybrid Learning PDF Author: Danuta Rutkowska
Publisher: Physica
ISBN: 379081802X
Category : Computers
Languages : en
Pages : 292

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Book Description
The advent of the computer age has set in motion a profound shift in our perception of science -its structure, its aims and its evolution. Traditionally, the principal domains of science were, and are, considered to be mathe matics, physics, chemistry, biology, astronomy and related disciplines. But today, and to an increasing extent, scientific progress is being driven by a quest for machine intelligence - for systems which possess a high MIQ (Machine IQ) and can perform a wide variety of physical and mental tasks with minimal human intervention. The role model for intelligent systems is the human mind. The influ ence of the human mind as a role model is clearly visible in the methodolo gies which have emerged, mainly during the past two decades, for the con ception, design and utilization of intelligent systems. At the center of these methodologies are fuzzy logic (FL); neurocomputing (NC); evolutionary computing (EC); probabilistic computing (PC); chaotic computing (CC); and machine learning (ML). Collectively, these methodologies constitute what is called soft computing (SC). In this perspective, soft computing is basically a coalition of methodologies which collectively provide a body of concepts and techniques for automation of reasoning and decision-making in an environment of imprecision, uncertainty and partial truth.

Neuro-fuzzy Controllers

Neuro-fuzzy Controllers PDF Author: Jelena Godjevac
Publisher: EPFL Press
ISBN: 9782880743550
Category : Fuzzy logic
Languages : en
Pages : 172

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Book Description


Expert Systems

Expert Systems PDF Author: Cornelius T. Leondes
Publisher: Elsevier
ISBN: 0080531458
Category : Computers
Languages : en
Pages : 2125

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Book Description
This six-volume set presents cutting-edge advances and applications of expert systems. Because expert systems combine the expertise of engineers, computer scientists, and computer programmers, each group will benefit from buying this important reference work. An "expert system" is a knowledge-based computer system that emulates the decision-making ability of a human expert. The primary role of the expert system is to perform appropriate functions under the close supervision of the human, whose work is supported by that expert system. In the reverse, this same expert system can monitor and double check the human in the performance of a task. Human-computer interaction in our highly complex world requires the development of a wide array of expert systems. Expert systems techniques and applications are presented for a diverse array of topics including Experimental design and decision support The integration of machine learning with knowledge acquisition for the design of expert systems Process planning in design and manufacturing systems and process control applications Knowledge discovery in large-scale knowledge bases Robotic systems Geograhphic information systems Image analysis, recognition and interpretation Cellular automata methods for pattern recognition Real-time fault tolerant control systems CAD-based vision systems in pattern matching processes Financial systems Agricultural applications Medical diagnosis

Flexible Neuro-Fuzzy Systems

Flexible Neuro-Fuzzy Systems PDF Author: Leszek Rutkowski
Publisher: Springer Science & Business Media
ISBN: 1402080425
Category : Computers
Languages : en
Pages : 286

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Book Description
Flexible Neuro-Fuzzy Systems is the first professional literature about the new class of powerful, flexible fuzzy systems. The author incorporates various flexibility parameters to the construction of neuro-fuzzy systems. This approach dramatically improves their performance, allowing the systems to perfectly represent the pattern encoded in data. Flexible Neuro-Fuzzy Systems is the only book that proposes a flexible approach to fuzzy modeling and fills the gap in existing literature. This book introduces new fuzzy systems which outperform previous approaches to system modeling and classification, and has the following features: -Provides a framework for unification, construction and development of neuro-fuzzy systems; -Presents complete algorithms in a systematic and structured fashion, facilitating understanding and implementation, -Covers not only advanced topics but also fundamentals of fuzzy sets, -Includes problems and exercises following each chapter, -Illustrates the results on a wide variety of simulations, -Provides tools for possible applications in business and economics, medicine and bioengineering, automatic control, robotics and civil engineering.

Fuzzy Logic and its Applications to Engineering, Information Sciences, and Intelligent Systems

Fuzzy Logic and its Applications to Engineering, Information Sciences, and Intelligent Systems PDF Author: Zeungnam Bien
Publisher: Springer Science & Business Media
ISBN: 9400901259
Category : Mathematics
Languages : en
Pages : 472

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Book Description
Fuzzy technology has emerged as one of the most exciting new concepts available. Fuzzy Logic and its Applications... covers a wide range of the theory and applications of fuzzy logic and related systems, including industrial applications of fuzzy technology, implementing human intelligence in machines and systems. There are four main themes: intelligent systems, engineering, mathematical foundations, and information sciences. Both academics and the technical community will learn how and why fuzzy logic is appreciated in the conceptual, design and manufacturing stages of intelligent systems, gaining an improved understanding of the basic science and the foundations of human reasoning.

Mechatronics And Manufacturing Technologies - Proceedings Of The International Conference (Mmt 2016)

Mechatronics And Manufacturing Technologies - Proceedings Of The International Conference (Mmt 2016) PDF Author: Poki Chen
Publisher: World Scientific
ISBN: 9813222360
Category : Technology & Engineering
Languages : en
Pages : 750

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Book Description
Held in Wuhan of China from August 20-21, 2016, the 2016 International Conference on Mechatronics and Manufacturing Technologies (MMT2016) provides an excellent international academic forum for all the researchers and practitioners to share resources, exchange opinions and inspire studying.The conference enjoys a wide spread participation among all over the universities and research institutes. It provides a broad overview of the latest research results on related fields and also a significant platform for academic connection and exchange.MMT2016 proceedings collects together 96 articles, after peer-review, to report on state-of-art developments of mechanical engineering based on originality, significance and clarity for the purpose of the Conference.

Fuzzy Neural Networks for Real Time Control Applications

Fuzzy Neural Networks for Real Time Control Applications PDF Author: Erdal Kayacan
Publisher: Butterworth-Heinemann
ISBN: 0128027037
Category : Mathematics
Languages : en
Pages : 266

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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