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 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.