[内容简介]
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
[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.