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Intelligent Control Systems Using Soft Computing MethodologiesIntelligent Control Systems Using Soft Computing Methodologies Edited by Ali Zilouchian Mo Jamshidi CRC Press Boca Raton London New York Washington, D.C. Library of Congress Cataloging-in-Publication Data Intelligent control systems using soft computing methodologies / edited by Ali Zilouchian and Mohammad Jamshidi. p. cm. Includes bibliographical references and index. ISBN 0-8493-1875-0 1. Intelligent control systems—Data processing. 2. Soft computing. I. Zilouchian, Ali. II. Jamshidi, Mohammad. TJ217.5 .I5435 2001 629.89′0285′63—dc21 2001016189 This book contains information obtained from authentic and highly regarded sources. Reprinted material is quoted with permission, and sources are indicated. A wide variety of references are listed. Reasonable efforts have been made to publish reliable data and information, but the author and the publisher cannot assume responsibility for the validity of all materials or for the consequences of their use. Neither this book nor any part may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, microfilming, and recording, or by any information storage or retrieval system, without prior permission in writing from the publisher. All rights reserved. Authorization to photocopy items for internal or personal use, or the personal or internal use of specific clients, may be granted by CRC Press LLC, provided that .50 per page photocopied is paid directly to Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA 01923 USA. The fee code for users of the Transactional Reporting Service is ISBN 0-8493-1875- 0/01/0.00+.50. The fee is subject to change without notice. For organizations that have been granted a photocopy license by the CCC, a separate system of payment has been arranged. The consent of CRC Press LLC does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific permission must be obtained in writing from CRC Press LLC for such copying. Direct all inquiries to CRC Press LLC, 2000 N.W. Corporate Blvd., Boca Raton, Florida 33431. Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation, without intent to infringe. Visit the CRC Press Web site at www.crcpress.com © 2001 by CRC Press LLC No claim to original U.S. Government works International Standard Book Number 0-8493-1875-0 Library of Congress Card Number 2001016189 Printed in the United States of America 1 2 3 4 5 6 7 8 9 0 Printed on acid-free paper To my late grandfather, Gholam-Reza for his devotion to science and humanitarian causes A. Zilouchian To my family, Jila, Ava and Nima for their love and patience M. Jamshidi PREFACE Since the early 1960s, artificial intelligence (AI) has found its way into industrial applications − mostly in the area of expert knowledge-based decision making for the design and monitoring of industrial products or processes. That fact has been enhanced with advances in computer technology and the advent of personal computers, and many applications of intelligence have been realized. With the invention of fuzzy chips in the1980s, fuzzy logic received a high boost in industry, especially in Japan. In this country, neural networks and evolutionary computations were also receiving unprecedented attention in both academia and industry. As a result of these events, “soft computing” was born. st Now at the dawn of the 21 century, soft computing continues to play a major role in modeling, system identification, and control of systems − simple or complex. The significant industrial uses of these new paradigms have been found in the U.S.A and Europe, in addition to Japan. However, to be able to design systems having high MIQ® (machine intelligence quotient, a concept first introduced by Lotfi Zadeh), a profound change in the orientation of control theory may be required. The principal constituents of soft computing are fuzzy logic, neurocomputing, genetic algorithms, genetic programming, chaos theory, and probabilistic reasoning. One of the principal components of soft computing is fuzzy logic. The role model for fuzzy logic is the human mind. From a control theoretical point of view, fuzzy logic has been intermixed with all the important aspects of systems theory: modeling, identification, analysis, stability, synthesis, filtering, and estimation. Interest in stability criteria for fuzzy control systems has grown in recent years. One of the most important difficulties with the creation of new stability criteria for any fuzzy control system has been the analytical interpretation of the linguistic part of fuzzy controller IF-THEN rules. Often fuzzy control systems are designed with very modest or no prior knowledge of a solid mathematical model, which, in turn, makes it relatively difficult to tap into many tools for the stability of conventional control systems. With the help of Takagi-Sugeno fuzzy IF-THEN rules in which the consequences are analytically derived, sufficient conditions to check the stability of fuzzy control systems are now available. These schemes are based on the stability theory of interval matrices and those of the Lyapunov approach. Frequency-domain methods such as describing functions are also being employed for this purpose. This volume constitutes a report on the principal elements and important applications of soft computing as reported from some of the active members of this community. In its chapters, the book gives a prime introduction to soft computing with its principal components of fuzzy logic, neural networks, genetic algorithms, and genetic programming with some textbook-type problems given. There are also many industrial and development efforts in the applications of intelligent systems through soft computing given to guide the interested readers on their research interest track. This book provides a general foundation of soft computing methodologies as well as their applications, recognizing the multidisciplinary nature of the subject. The book consists of 21 chapters, organized as follows: In Chapter 1, an overview of intelligent control methodologies is presented. Various design and implementation issues related to controller design for industrial applications using soft computing techniques are briefly discussed in this chapter. Furthermore, an overall evaluation of the intelligent systems is presented therein. The next two chapters of the book focus on the fundamentals of neural networks (NN). Theoretical as well as various design issues related to NN are discussed. In general, NN are composed of many simple elements emulating various brain activities. They exploit massive parallel local processing and distributed representation properties that are believed to exist in the brain. The primary purpose of NN is to explore and produce human information processing tasks such as speech, vision, knowledge processing, and motor control. The attempt of organizing human information processing tasks highlights the classical comparison between information processing capabilities of the human and so called hard computing. The computer can multiply large numbers at fast speed, yet it may not be capable to understand an unconstrained pattern such as speech. On the other hand, though humans understand speech, they lack the ability to compute the square root of a prime number without the aid of pencil and paper or a calculator. The difference between these two opposing capabilities can be traced to the processing methods which each employs. Digital computers rely upon algorithm-based programs that operate serially, are controlled by CPU, and store the information at a particular location in memory. On the other hand, the brain relies on highly distributed representations and transformations that operate in parallel, have distributed control through billions of highly interconnected neurons or processing elements, and store information in various straight connections called synapses. Chapter 2 is devoted to the fundamental issues above. In Chapter 3, supervised learning with emphasis on back propagation and radial basis neural functions algorithms is presented. This chapter also addresses unsupervised learning (Kohonen self-organization) and recurrent networks (Hopfield). In Chapters 4 − − − − 7, several applications of neural networks are presented in order to familiarize the reader with design and implementation issues as well as applicability of NN to science and engineering. These applications areas include medicine and biology (Chapter 4), digital signal processing (Chapter 5), computer networking (Chapter 6), and oil refinery (Chapter 7). Chapters 8, 9 and 10 of the book are devoted to the theoretical aspect of fuzzy set and fuzzy logic (FL). The main objective of these three chapters is to provide the reader with sufficient background related to implementation issues in the following chapters. In these chapters, we cover the fundamental concepts of fuzzy sets, fuzzy relation, fuzzy logic, fuzzy control, fuzzification, defuzification, and stability of fuzzy systems. As is well known, the first implementation of Professor Zadeh’s idea pertaining to fuzzy sets and fuzzy logic was accomplished in 1975 by Mamedani, who demonstrated the viability of fuzzy logic control (FLC) for a small model steam engine. After this pioneer work, many consumer products as well as other high tech applications using fuzzy technology have been developed and are currently available on the market. In Chapters 11 − − − − 16, several recent industrial applications of fuzzy logic are presented. These applications include navigation of autonomous planetary rover (Chapter 11), autonomous underwater vehicle (Chapter 12), management of air conditioning, heating and cooling systems (Chapter 13), robot manipulators (Chapter 14), desalination of seawater (Chapter 15), and object recognition (Chapter 16). Chapter 17 presents a brief introduction to evolutionary computations. In Chapters (18 − − − − 20), several applications of evolutionary computations are explored. The integration of these methodologies with fuzzy logic is also presented in these chapters. Finally, some examples and exercises are provided in Chapter 21. MATLAB neural network and fuzzy logic toolboxes have been utilized to solve several problems. The editors would like to take this opportunity to thank all the authors for their contributions to this volume and to the soft computing area. We would like to thank Professor Lotfi A. Zadeh for his usual visionary ideas and support. The encouragement and patience of CRC Press Editor Nora Konopka is very much appreciated. Without her continuous help and assistance during the entire course of this project, we could not have accomplished the task of integrating various chapters into this volume. The editors are also indebted to many who helped us realize this volume. Hooman Yousefizadeh, a Ph.D. student at FAU, has modified several versions of various chapters of the book and organized them in camera-ready format. Without his dedicated help and commitment, the production of the book would have taken a great deal longer. We sincerely thank Robert Caltagirone, Helena Redshaw, and Shayna Murry from CRC Press for their assistance. We would like to also thank the project editor, Judith Simon Kamin from CRC Press for her commitment and skillful effort of editing and processing several iterations of the manuscript. Finally, we are indebted to our family for their constant support and encouragement throughout the course of this project. Ali Zilouchian Mo Jamshidi Boca Raton, FL Albuquerque, NM ABOUT THE EDITORS Ali Zilouchian is currently a professor and the director of the Intelligent Control laboratory funded by the National Science Foundation (NSF) in the department of electrical engineering at Florida Atlantic University, Boca Raton, FL. His recent works involve the applications of soft computing methodologies to industrial processes including oil refineries, desalination processes, fuzzy control of jet engines, fuzzy controllers for car engines, kinematics and dynamics of serial and parallel robot manipulators. Dr. Zilouchian’s research interests include the industrial applications of intelligent controls using neural network, fuzzy logic, genetic algorithms, data clustering, multidimensional signal processing, digital filtering, and model reduction of large scale systems. His recent projects have been funded by NSF and Motorola Inc. as well as several other sources. He has taught more than 22 different courses in the areas of intelligent systems, controls, robotics, computer vision, digital signal processing, and electronic circuits at Florida Atlantic University and George Washington University. He has supervised 13 Ph.D. and M.S. students during the last 15 years. In addition, he has served as a committee member on more than 25 MS theses and Ph.D. dissertations. He has published over 100 book chapters, textbooks, scholarly journal papers, and refereed conference proceedings. In 1996, Dr. Zilouchian was honored with a Florida Atlantic University Award for Excellence in Undergraduate Teaching. Dr. Zilouchian is a senior member of IEEE, member of Sigma Xi and New York Academy of Science and Tau Beta Pi. He received the outstanding leadership award for IEEE branch membership development activities for Region III in 1988. He has served as session chair and organizer of nine different sessions in the international conferences within the last five years. He was a keynote speaker at the International Conference on Seawater Desalination Technologies in November 2000. Dr. Zilouchian is currently an associate editor of the International Journal of Electrical and Computer Engineering out of Oxford, UK. He is also the local chairman of the next WAC 2002 to be held in June 2002 in Orlando, Florida. Mohammad (Mo) Jamshidi (Fellow IEEE, Fellow ASME, Fellow AAAS) earned a Ph.D. degree in electrical engineering from the University of Illinois at Urbana-Champaign in February 1971. He holds an honorary doctorate degree from Azerbaijan National University, Baku, Azerbaijan, 1999. Currently, he is the Regents professor of electrical and computer engineering, the AT&T professor of manufacturing engineering, professor of mechanical engineering and founding director of the NASA Center for Autonomous Control Engineering (ACE) at the University of New Mexico, Albuquerque. He was on the advisory board of NASA JPL's Pathfinder Project mission, which landed on Mars on July 4, 1997. He is currently a member of the NASA Minority Businesses Resource Advisory Committee and a member of the NASA JPL Surface Systems Track Review Board. He was on the USA National Academy of Sciences NRC's Integrated Manufacturing Review Board. Previously he spent 6 years at U.S. Air Force Phillips (formerly Weapons) Laboratory working on large scale systems, control of optical systems, and adaptive optics. He has been a consultant with the Department of Energy’s Los Alamos National Laboratory and Oak Ridge National Laboratory. He has worked in various academic and industrial positions at various national and international locations including with IBM and GM Corporations. He has contributed to over 475 technical publications including 45 books and edited volumes. Six of his books have been translated into at least one foreign language. He is the founding editor, co-founding editor, or editor-in- chief of five journals (including Elsevier's International Journal of Computers and Electrical Engineering) and one magazine (IEEE Control Systems Magazine). He has been on the executive editorial boards of a number of journals and two encyclopedias. He was the series editor for ASME Press Series on Robotics and Manufacturing from 1988 to 1996 and Prentice Hall Series on Environmental and Intelligent Manufacturing Systems from 1991 to 1998. In 1986 he helped launch a specialized symposium on robotics which was expanded to International Symposium on Robotics and Manufacturing (ISRAM) in 1988, and since 1994, it has been expanded into the World Automation Congress (WAC) where it now encompasses six main symposia and forums on robotics, manufacturing, automation, control, soft computing, and multimedia and image processing. He has been the general chairman of WAC from its inception. Dr. Jamshidi is a fellow of the IEEE for contributions to "large-scale systems theory and applications and engineering education," a fellow of the ASME for contributions to “control of robotic and manufacturing systems,” a fellow of the AAAS − the American Association for the Advancement of Science − for contributions to "complex large-scale systems and their applications to controls and optimization". He is also an associate fellow of Third World Academy of Sciences (Trieste, Italy), member of Russian Academy of Nonlinear Sciences, associate fellow, Hungarian Academy of Engineering, corresponding member of the Persian Academies of Science and Engineering, a member of the New York Academy of Sciences and recipient of the IEEE Centennial Medal and IEEE Control Systems Society Distinguished Member Award and the IEEE CSS Millennium Award. He is an honorary professor at three Chinese universities. He is on the board of Nobel Laureate Glenn T. Seaborg Hall of Science for Native American Youth. CONTRIBUTORS Akbarzadeh-T, Mohammad Hildebrand, Lars Department of EECE University of Dortmund Ferdowsi University Dortmund, Germany Mashad, Iran Homaifar, Abdollah Battle, Darryl Department of Electrical Department of Electrical Engineering Engineering North Carolina A&T University North Carolina A&T University Greensboro, NC Greensboro, NC Howard, Ayanna Bawazir, Khalid Jet Propulsion Laboratory Aramco Pasadena, CA Dhahran, Saudi Arabia Howard, David Chen, Tan Kay Department of Electrical The National Engineering University of Singapore Florida Atlantic University Singapore Boca Raton, FL Dozier, Gerry Jafar, Mutaz Computer Science and Kuwait Institute of Software Engineering Scientific Research Auburn University Kuwait City, Kuwait Auburn, AL Jamshidi, Mohammad El-Osery, Aly Department of Electrical and Department of Electrical and Computer Engineering Computer Engineering University of New Mexico University of New Mexico Albuquerque, NM Albuquerque, NM Lee, T.H. Fathi, Madjid The National Department of Electrical and University of Singapore Computer Engineering Singapore University of New Mexico Albuquerque, NM Meghdadi, A. H. Tunstel, Edward Department of Electrical Jet Propulsion Laboratory Engineering Pasadena, CA Ferdowsi University Mashad, Iran Valafar, Faramarz Department of Cognitive and Ross, Timothy J. Neural Systems Department of Civil Engineering Boston University University of New Mexico Boston, MA Albuquerque, NM Wang, Dali Seraji, Homayoun STM Wireless, Inc. Jet Propulsion Laboratory Irvine, CA Pasadena, CA Wang, M. L. Smith, Samuel M. The National Institute for Ocean and University of Singapore Systems Engineering Singapore Florida Atlantic University Dania, FL Yousefizadeh, Homayoun Procom Technology, Inc. Song, Feijun Santa Ana, CA Institute for Ocean and Systems Engineering Yousefizadeh, Hooman Florida Atlantic University, Department of Electrical Dania, FL Engineering Florida Atlantic University Talebi-Daryani, Reza Boca Raton, FL Department of Control Engineering University of Applied Sciences Zilouchian, Ali Cologne, Germany Department of Electrical Engineering Tan, K. C. Florida Atlantic University The National Boca Raton, FL University of Singapore Singapore ABBREVIATIONS 1D One Dimension Two Dimension 2D A/C Air Conditioning Average Changes in Slope ACS ADALINE ADAptive LINear Element Artificial Intelligence AI ANFIS Adaptive Neuro-Fuzzy Inference System Artificial Neural Network ANN AUV Autonomous Underwater Vehicle Back Propagation BP BPA Back Propagation Algorithm Constant Bit Rate CBR CCSN Common Channel Signaling Network Complete Partitioning CP CP Candidate Path Causal Recursive Digital Filters CRDF CS Complete Sharing Cellulose Triacetate CT CV Containment Value Derivative D DCS Distributed Control Systems Distributed Digital Control DDC DNS Dynamic Neural Sharing Degree Of Freedom DOF EA Evolutionary Algorithm Estimated Average Latency EAL EC Evolutionary Computation Electrodialysis ED FAM Fuzzy Associate Memory Fractal Gaussian Noise FGN FIR Finite Impulse Response Fuzzy Inference System FIS FL Fuzzy Logic Fuzzy Logic Controller FLC FNF False Negative Fraction Field of View FOV FPF False Positive Fraction Fuzzy Rule Based System FRBS FTDM Fixed Time Division Multiplexing Fuzzy Tournament Selection Algorithm FTSA GA Genetic Algorithm Gas Chromatography-Electron Impact Mass GC-EIMS Spectroscopy Global Evolutionary Planning and Obstacle GEPOA Avoidance GP Genetic Programming GPD Gallon Per Day Gallon Per Minute GPM HFF Hollow Fine Fiber HIS Health and Safety Indicators I Integral IE Ion Exchange IIR Infinite Impulse Response Least Mean Square LMS LSS Local State Space MADALINE Multiple ADALINE MAL Measured Average Latency MCV Mean Cell Volume ME Multi- Effect Membership Function MF MFC Membership Function Chromosome MIMO Multi Input Multi Output MISO Multi Input Single Output MLE Maximum Likelihood Types Estimates MSF Multi- Stage Flash Negative Big NB NL Negative Large NM Negative Medium NMR Nuclear Magnetic Resonance Neural Network NN NS Negative Small Optimal Associative Memory OAM OEX Ocean Explorer OR Operations Research P Proportional Predictive Accuracy PA PB Positive Big Piecewise Continuous Polynomial PCP PD Proportional Derivative PE Processing Element PI Proportional Integral Proportional Integral-Derivative PID PL Positive Large Programmable Logic Controller PLC PM Positive Medium PS Positive Small PSI Pressure Per Square Inch Predictive Value PV RBFN Radial Basis Function Network Radius of Influence RI RMS Recursive Mean Square RO Reverse Osmosis ROC Receiver Operating Characteristic Read Vapor Pressure RVP SCADA Supervisory Control and Data Acquisition SCS Sum of the Changes in Slope SDF Separable-in-Denominator Digital Filters SDI Silt Density Index SGA Simple Genetic Algorithm Sliding Mode Controller SMC SMFC Sliding Mode Fuzzy Controller SPS Static Partial Sharing STDM Statistical Time Division Multiplexing SW Spiral Wound TC Time Control Temperature Correction Factor TCF TDS Total Dissolved Solid TNF True Negative Function TPF True Positive Function TS Takagi-Sugeno VBR Variable Bit Rate Visibility Base Repair VBR VC Vapor Compressions VSC Variable Structure Controller XOR Exclusive Or TABLE OF CONTENTS Chapter 1 INTRODUCTION Ali Zilouchian and Mo Jamshidi 1.1 Motivation 1.2 Neural Networks 1.2.1 Rationale for Using NN in Engineering 1.3 Fuzzy Logic Control 1.3.1 Rationale for Using FL in Engineering 1.4 Evolutionary Computation 1.5 Hybrid Systems 1.6 Organization of the Book References Chapter 2 FUNDAMENTALS OF NEURAL NETWORKS Ali Zilouchian 2.1 Introduction 2.2 Basic Structure of a Neuron 2.2.1 Model of Biological Neurons 2.2.2 Elements of Neural Networks WeightingFactors Threshold Activation Function 2.3 ADALINE 2.4 Linear Separable Patterns 2.5 Single Layer Perceptron 2.5.1 General Architecture 2.5.2 Linear Classification 2.5.3 Perceptron Algorithm 2.6 Multi-Layer Perceptron 2.6.1 General Architecture 2.6.2 Input-Output Mapping 2.6.3 XOR Realization 2.7 Conclusion References Chapter 3 NEURAL NETWORK ARCHITECTURES Hooman Yousefizadeh and Ali Zilouchian 3.1 Introduction 3.2 NN Classifications 3.2.1 Feedforward and feedback networks 3.2.2 Supervised and Unsupervised Learning Networks 3.3 Back Propagation Algorithm 3.3.1 Delta Training Rule 3.4 Radial Basis Function Network (RBFN) 3.5 Kohonen Self Organization Network 3.5.1 Training of the Kohonen Network 3.5.2 Examples of Self-Organization 3.6 Hopfield Network 3.7 Conclusions References Chapter 4 APPLICATIONS OF NEURAL NETWORKS IN MEDICINE AND BIOLOGICAL SCIENCES Faramarz Valafar 4.1 Introduction 4.2. Terminology and Standard Measures 4.3 Recent Neural Network Research Activity in Medicine and Biological Sciences 4.3.1 ANNs in Cancer Research 4.3.2 ANN Biosignal Detection and Correction 4.3.3 Decision-making in Medical Treatment Strategies 4.4 Summary References Chapter 5 APPLICATION OF NEURAL NETWORK IN DESIGN OF DIGITAL FILTERS Dali Wang and Ali Zilouchian 5.1 Introduction 5.2 Problem Approach 5.2.1 Neural Network for Identification 5.2.2 Neural Network Structure 5.3 A Training Algorithm for Filter Design 5.3.1 Representation 5.3.2 Training Objective 5.3.3 Weight Adjustment 5.3.4 The Training Algorithm 5.4 Implementation Issues 5.4.1 Identifying a System in Canonical Form 5.4.2 Stability, Convergence, Learning Rate and Scaling 5.5 2-D Filter Design Using Neural Network 5.5.1 Two-imensional Signal and Digital Filters 5.5.2 Design Techniques 5.5.3 Neural Network Approach 5.6 Simulation Results 5.6.1 1-D Filters 5.6.2 2-D Filters 5.7 Conclusions References Chapter 6 APPLICATION OF COMPUTER NETWORKING USING NEURAL NETWORK Homayoun Yousefizadeh 6.1 Introduction 6.2 Self Similar Packet Traffic 6.2.1 Fractal Properties of Packet Traffic 6.2.2 Impacts of Fractal Nature of Packet Traffic 6.3 Neural Network Modeling of Packet Traffic 6.3.1 Perceptron Neural Networks and Back Propagation Algorithm 6.3.2 Modeling Individual Traffic Patterns 6.3.3 Modeling Aggregated Traffic Patterns 6.4 Applications of Traffic Modeling 6.4.1 Packet Loss Prevention 6.4.2 Packet Latency Prediction 6.4.3 Experimental Observations 6.5 Summary References Chapter 7 APPLICATION OF NEURAL NETWORKS IN OIL REFINERIES Ali Zilouchian and Khalid Bawazir 7.1 Introduction 7.2 Building the Artificial Neural Network 7.2.1 Range of Input Data 7.2.2 Size of the Training Data Set 7.2.3 Acquiring the Training Data Set 7.2.4 Validity of the Training Data Set 7.2.5 Selecting Process Variables 7.3 Data Analysis 7.3.1 Elimination of Bad Lab Values 7.3.2 Process Parameters’ Effect on Neural Network Prediction 7.4 Implementation Procedure 7.4.1 Identifying the Application 7.4.2 Model Inputs Identification 7.4.3 Range of Process Variables 7.5 Predictor Model Training 7.6 Simulation Results and Discussions 7.6.1 Naphtha 95% Cut Point 7.6.2 Naphtha Reid Vapor Pressure 7.7 Conclusions References Chapter 8 INTRODUCTION TO FUZZY SETS: BASIC DEFINITIONS AND RELATIONS Mo Jamshidi and Aly El-Osery 8.1 Introduction 8.2 ClassicalSets 8.3 Classical Set Operations 8.4 Properties of Classical Sets 8.5 FuzzySets 8.5.1 Fuzzy Membership Functions 8.6 Fuzzy Set Operations 8.7 Properties of Fuzzy Sets 8.7.1 Alpha-Cut FuzzySets 8.7.2 Extension Principle 8.8 Classical Relations vs. Fuzzy Relations 8.9 Conclusion References Chapter 9 INTRODUCTION TO FUZZY LOGIC Mo Jamshidi, Aly El-Osery, and Timothy J. Ross 9.1 Introduction 9.2 Predicate Logic 9.2.1 Tautologies 9.2.2 Contradictions 9.2.3 Deductive Inferences 9.3 Fuzzy Logic 9.4 Approximate Reasoning 9.5 Conclusion References Chapter 10 FUZZY CONTROL AND STABILITY Mo Jamshidi and Aly El-Osery 10.1 Introduction 10.2 Basic Definitions 10.2.1 InferenceEngine 10.2.2 Defuzzification 10.3 Fuzzy Control Design 10.4 Analysis of Fuzzy Control Systems 10.5 Stability of Fuzzy Control Systems 10.5.1 LyapunovStability 10.5.2 Stability via Interval Matrix Method 10.6 Conclusion References Chapter 11 SOFT COMPUTING APPROACH TO SAFE NAVIGATION OF AUTONOMOUS PLANETARY ROVERS Edward Tunstel, Homayoun Seraji, and Ayanna Howard 11.1 Introduction 11.1.1 Practical Issues in Planetary Rover Applications 11.2 Navigation System Overview 11.2.1 Fuzzy-Behaviour-Based Structure 11.3 Fuzzy -Logic-Based Rover Health and Safety 11.3.1 Health and Safety Indicators 11.3.2 Stable Attitude Control 11.3.3 Traction Management Neuro-Fuzzy Solution 11.4 Fuzzy Terrain-Based Navigation 11.4.1 Visual Terrain Traversability Assessment and Fuzzy Reasoning Terrain Roughness Extraction Terrain Slope Extraction Fuzzy Inference of Terrain Traversability 11.5 Strategic Fuzzy Navigation Behaviors 11.5.1 Seek-Goal Behavior 11.5.2 Traverse-Terrain Behavior 11.5.3 Avoid-Obstacle Behavior 11.5.4 Fuzzy-BehaviorFusion 11.6 Rover Test Bed and Experimental Results 11.6.1 Safe Mobility 11.6.2 Safe Navigation 11.7 Summary and Conclusions Acknowledgement References Chapter 12 AUTONOMOUS UNDERWATER VEHICLE CONTROL USING FUZZY LOGIC Feijun Song and Samuel M. Smith 12.1 Introduction 12.2 Background 12.3 Autonomous Underwater Vehicles (AUVs) 12.4 Sliding Mode Control 12.5 Sliding Mode Fuzzy Control (SMFC) 12.6 SMFC Design Examples 12.7 Guidelines for Online Adjustment 12.7.1 SlidingSlope λ Effects 12.7.2 Thickness of the Boundary Layer φ Effects 12.8 At Sea Experimental Results 12.9 Summary References Chapter 13 APPLICATION OF FUZZY LOGIC FOR CONTROL OF HEATING, CHILLING, AND AIR CONDITIONING SYSTEMS Reza Talebi-Daryani 13.1 Introduction 13.2 Building Energy Management System (BEMS) 13.2.1 System Requirements 13.2.2 System Configuration 13.2.3 Automation Levels 13.3 Air Conditioning System: FLC vs. DDC 13.3.1 Process Description 13.3.2 Process Control 13.3.3 Digital PID Controller 13.3.4 Fuzzy Cascade Controller 13.3.5 DDC vs. FLC 13.4 Fuzzy Control for the Operation Management of a Complex Chilling System 13.4.1 Process Description 13.4.2 Process Operation with FLC 13.4.3 Description of the Different Fuzzy Controllers

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