edited by S G Tzafestas (National Technical University of Athens)

Spyros G Tzafestas holds a BSc (Physics and Electronics); DIC, MSc, PhD and DSc (Systems and Control); DSc(Hon) and Dr.-Ing.E.h. He is Professor of Robotics and Control at the National Technical University of Athens (since 1985). From 1969 to 1973, he was research leader at the NRC Demokritos (Athens) and from 1973 to 1984, Professor of Control at Patras University, Greece. He has published 30 research books and over 500 technical papers. He is editor of the Journal of Intelligent and Robotic Systems, editor of the Kluwer MBSE book series, and associate editor of 15 journals. His research interests include intelligent systems, control, robotics and CIM. He is a member of the IEEE (Fellow), IEE (Fellow), ASME, SIRES (Honorary President), IMACS (T.C. Chairman) and NYAS.
 

Soft computing is a branch of computing which, unlike hard computing, can deal with uncertain, imprecise and inexact data. The three constituents of soft computing are fuzzy-logic-based computing, neurocomputing, and genetic algorithms. Fuzzy logic contributes the capability of approximate reasoning, neurocomputing offers function approximation and learning capabilities, and genetic algorithms provide a methodology for systematic random search and optimization. These three capabilities are combined in a complementary and synergetic fashion.

This book presents a cohesive set of contributions dealing with important issues and applications of soft computing in systems and control technology. The contributions include state-of-the-art material, mathematical developments, fresh results, and how-to-do issues. Among the problems studied via neural, fuzzy, neurofuzzy and genetic methodologies are: data fusion, reinforcement learning, approximation properties, multichannel imaging, signal processing, system optimization, gaming, and several forms of control.

The book can serve as a reference for researchers and practitioners in the field. Readers can find in it a large amount of useful and timely information, and thus save considerable effort in searching for other scattered literature.

• Neural Networks in System Identification and Control:
  • Supervised Learning in Multilayer Perceptrons: The Back-Propagation Algorithm (S G Tzafestas & Y Anthopoulos)
  • Identification of Two-Dimensional State Space Discrete Systems Using Neural Networks (D Wang & A Zilouchian)
  • Neural Networks for Control (R J Mitchell)
  • Neuro-Based Adaptive Regulator (T Tsuji et al.)
  • Local Model Networks and Self-Tuning Predictive Control (P J Gawthrop & E Ronco)
• Fuzzy and Neuro-Fuzzy Systems in Modeling, Control and Robot Path Planning:
  • An On-Line Self Constructing Fuzzy Modeling Architecture Based on Neural and Fuzzy Concepts and Techniques (S G Tzafestas & K C Zikidis)
  • Neuro-Fuzzy Model Based Control (D Matko et al.)
  • Fuzzy and Neurofuzzy Approaches to Mobile Robot Path and Motion Planning Under Uncertainty (C S Tzafestas & S G Tzafestas)
• Genetic-Evolutionary Algorithms:
  • A Tutorial Overview of Genetic Algorithms and Their Applications (S G Tzafestas et al.)
  • Results from a Variety of Genetic Algorithm Applications Showing the Robustness of the Approach (W D Potter et al.)
  • Evolutionary Algorithms in Computer-Aided Design of Integrated Circuits (R Drechsler et al.)
• Soft Computing Applications:
  • Soft Data Fusion (C G Looney & Y Varol)
  • Application of Neural Networks to Computer Gaming (N Baba)
  • Coherent Neural Networks and Their Applications to Control and Signal Processing (A Hirose)
  • Neural, Fuzzy and Evolutionary Reinforcement Learning Systems: An Application Case Study (D A Linkens & H O Nyongesa)
  • Neural Networks in Industrial and Environmental Applications (G C Smith & C L Wrobel)