Soft computing in systems and control technology
著者
書誌事項
Soft computing in systems and control technology
（World Scientific series in robotics and intelligent systems, v. 18）
World Scientific, c1999
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
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.
目次
- Neural networks in systems identification and control - supervised learning in multilayer perceptions - the back-propagation algorithm
- identification of two-dimensional state space discrete systems using neural networks
- neural networks for control
- neuro-based adaptive regulator
- local model networks and self-tuning predictive control
- fuzzy and neuro-fuzzy systems in modelling, control and robot path planning - an on-line self constructing fuzzy modelling architecture based on neural and fuzzy concepts and techniques
- neuro-fuzzy model-based control
- fuzzy and neurofuzzy approaches to mobile robot path and motion planning under uncertainty
- genetic-evolutionary algorithms - a tutorial overview of genetic algorithms and their applications
- results from a variety of genetic algorithm applications showing the robustness of the approach
- evolutionary algorithms in computer-aided design of integrated circuits
- soft computing applications - soft data fusion
- application of neural networks to computer gaming
- coherent neural networks and their applications to control and signal processing
- neural, fuzzy and evolutionary reinforcement learning systems - an application case study
- neural networks in industrial and environmental applications.
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