Analysis and Synthesis of Fuzzy Control Systems A Model-Based Approach

Fuzzy logic control (FLC) has proven to be a popular control methodology for many complex systems in industry, and is often used with great success as an alternative to conventional control techniques. However, because it is fundamentally model free, conventional FLC suffers from a lack of tools for systematic stability analysis and controller design. To address this problem, many model-based fuzzy control approaches have been developed, with the fuzzy dynamic model or the Takagi and Sugeno (TS) fuzzy model-based approaches receiving the greatest attention. Analysis and Synthesis of Fuzzy Control Systems: A Model-Based Approachoffers a unique reference devoted to the systematic analysis and synthesis of model-based fuzzy control systems. After giving a brief review of the varieties of FLC, including the TS fuzzy model-based control, it fully explains the fundamental concepts of fuzzy sets, fuzzy logic, and fuzzy systems. This enables the book to be self-contained and provides a basis for later chapters, which cover: TS fuzzy modeling and identification via nonlinear models or data Stability analysis of TS fuzzy systems Stabilization controller synthesis as well as robust H and observer and output feedback controller synthesis Robust controller synthesis of uncertain TS fuzzy systems Time-delay TS fuzzy systems Fuzzy model predictive control Robust fuzzy filtering Adaptive control of TS fuzzy systems A reference for scientists and engineers in systems and control, the book also serves the needs of graduate students exploring fuzzy logic control. It readily demonstrates that conventional control technology and fuzzy logic control can be elegantly combined and further developed so that disadvantages of conventional FLC can be avoided and the horizon of conventional control technology greatly extended. Many chapters feature application simulation examples and practical numerical examples based on MATLAB.

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Advances in Fuzzy Logic, Neural Networks and Genetic Algorithms

This book presents 14 rigorously reviewed revised papers selected from more than 50 submissions for the 1994 IEEE/ Nagoya-University World Wisepersons Workshop, WWW'94, held in August 1994 in Nagoya, Japan. The combination of approaches based on fuzzy logic, neural networks and genetic algorithms are expected to open a new paradigm of machine learning for the realization of human-like information processing systems. The first six papers in this volume are devoted to the combination of fuzzy logic and neural networks; four papers are on how to combine fuzzy logic and genetic algorithms. Four papers investigate challenging applications of fuzzy systems and of fuzzy-genetic algorithms.

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Neural Networks in Finance Gaining Predictive Edge in the Market.

[back jacket] Business/Finance Neural Networks in Finance Gaining Predictive Edge in the Market Paul McNelis "This book clarifies many of the mysteries of Neural Networks and related optimization techniques for researchers in both economics and finance. It contains many practical examples backed up with computer programs for readers to explore. I recommend it to anyone who wants to understand methods used in nonlinear forecasting." - Blake LeBaron, Professor of Finance, Brandeis University "An important addition to the select collection of books on financial econometrics. Neural Networks in Finance serves as an important reference on neural network models of nonlinear dynamics as a practical econometric tool for better decision-making in financial markets." - Roberto S. Mariano, Dean of School of Economics and Social Sciences & Vice-Provost for Research, Singapore Management University; Professor Emeritus of Economics, University of Pennsylvania Neural Networks in Finance explores the intuitive appeal of neural networks and the genetic algorithm in finance. It demonstrates how neural networks used in combination with evolutionary computation outperform classical econometric methods for accuracy in forecasting, classification and dimensionality reduction. The text shows that these networks are easy to implement and interpret once the time-honored quest for closed form solutions is reconsidered. McNelis utilizes a variety of examples, from forecasting automobile production and corporate bond spread, to inflation and deflation processes in Hong Kong and Japan, to credit card default in Germany, to bank failures in Texas, to cap-floor volatilities in New York and Hong Kong. Numerical illustrations use MATLAB code and the book is accompanied by a website.

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A first course in fuzzy and neural control

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Petri Net, Theory and Applications

Edited by: Vedran Kordic



ISBN 978-3-902613-12-7, Hard cover, 534 pages

Publisher: InTech

Publication date: February 2008

Although many
other models of concurrent and distributed systems have been de- veloped
since the introduction in 1964 Petri nets are still an essential model
for concurrent systems with respect to both the theory and the
applications. The main attraction of Petri nets is the way in which the
basic aspects of concurrent systems are captured both conceptually and
mathematically. The intuitively appealing graphical notation makes Petri
nets the model of choice in many applications. The natural way in which
Petri nets allow one to formally capture many of the basic notions and
issues of concurrent systems has contributed greatly to the development
of a rich theory of concurrent systems based on Petri nets.
This book brings together reputable researchers from all over the world
in order to provide a comprehensive coverage of advanced and modern
topics not yet reflected by other books. The book consists of 23
chapters written by 53 authors from 12 different countries.

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Petri Nets Applications

Edited by: Pawel Pawlewski



ISBN 978-953-307-047-6, Hard cover, 752 pages

Publisher: InTech

Publication date: February 2010









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Fuzzy Systems

Edited by: Ahmad Taher Azar



ISBN 978-953-7619-92-3, Hard cover, 216 pages

Publisher: InTech

Publication date: February 2010









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Self-Organizing Maps

Edited by: George K Matsopoulos



ISBN 978-953-307-074-2, Hard cover, 430 pages

Publisher: InTech

Publication date: April 2010









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Self Organizing Maps - Applications and Novel Algorithm Design

Edited by: Josphat Igadwa Mwasiagi



ISBN 978-953-307-546-4, Hard cover, 702 pages

Publisher: InTech

Publication date: January 2011

Kohonen Self
Organizing Maps (SOM) has found application in practical all fields,
especially those which tend to handle high dimensional data. SOM can be
used for the clustering of genes in the medical field, the study of
multi-media and web based contents and in the transportation industry,
just to name a few. Apart from the aforementioned areas this book also
covers the study of complex data found in meteorological and remotely
sensed images acquired using satellite sensing. Data management and
envelopment analysis has also been covered. The application of SOM in
mechanical and manufacturing engineering forms another important area of
this book. The final section of this book, addresses the design and
application of novel variants of SOM algorithms.

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Fuzzy Controllers, Theory and Applications

Edited by: Lucian Grigorie



ISBN 978-953-307-543-3, Hard cover, 368 pages

Publisher: InTech

Publication date: February 2011

Trying to meet the
requirements in the field, present book treats different fuzzy control
architectures both in terms of the theoretical design and in terms of
comparative validation studies in various applications, numerically
simulated or experimentally developed. Through the subject matter and
through the inter and multidisciplinary content, this book is addressed
mainly to the researchers, doctoral students and students interested in
developing new applications of intelligent control, but also to the
people who want to become familiar with the control concepts based on
fuzzy techniques. Bibliographic resources used to perform the work
includes books and articles of present interest in the field, published
in prestigious journals and publishing houses, and websites dedicated to
various applications of fuzzy control. Its structure and the presented
studies include the book in the category of those who make a direct
connection between theoretical developments and practical applications,
thereby constituting a real support for the specialists in artificial
intelligence, modelling and control fields.

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Artificial Neural Networks - Industrial and Control Engineering Applications

Edited by: Kenji Suzuki



ISBN 978-953-307-220-3, Hard cover, 478 pages

Publisher: InTech

Publication date: April 2011

Artificial neural
networks may probably be the single most successful technology in the
last two decades which has been widely used in a large variety of
applications. The purpose of this book is to provide recent advances of
artificial neural networks in industrial and control engineering
applications. The book begins with a review of applications of
artificial neural networks in textile industries. Particular
applications in textile industries follow. Parts continue with
applications in materials science and industry such as material
identification, and estimation of material property and state, food
industry such as meat, electric and power industry such as batteries and
power systems, mechanical engineering such as engines and machines, and
control and robotic engineering such as system control and
identification, fault diagnosis systems, and robot manipulation. Thus,
this book will be a fundamental source of recent advances and
applications of artificial neural networks in industrial and control
engineering areas. The target audience includes professors and students
in engineering schools, and researchers and engineers in industries.

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Fuzzy Algorithms for Control

Fuzzy Algorithms for Control gives an overview of the research results of a number of European research groups that are active and play a leading role in the field of fuzzy modeling and control. It contains 12 chapters divided into three parts. Chapters in the first part address the position of fuzzy systems in control engineering and in the AI community. State-of-the-art surveys on fuzzy modeling and control are presented along with a critical assessment of the role of these methodologists in control engineering. The second part is concerned with several analysis and design issues in fuzzy control systems. The analytical issues addressed include the algebraic representation of fuzzy models of different types, their approximation properties, and stability analysis of fuzzy control systems. Several design aspects are addressed, including performance specification for control systems in a fuzzy decision-making framework and complexity reduction in multivariable fuzzy systems. In the third part of the book, a number of applications of fuzzy control are presented. It is shown that fuzzy control in combination with other techniques such as fuzzy data analysis is an effective approach to the control of modern processes which present many challenges for the design of control systems. One has to cope with problems such as process nonlinearity, time-varying characteristics for incomplete process knowledge. Examples of real-world industrial applications presented in this book are a blast furnace, a lime kiln and a solar plant. Other examples of challenging problems in which fuzzy logic plays an important role and which are included in this book are mobile robotics and aircraft control. The aim of this book is to address both theoretical and practical subjects in a balanced way. It will therefore be useful for readers from the academic world and also from industry who want to apply fuzzy control in practice.

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