Hybrid Soft Computing Approach to Identification and
Control of Nonlinear Systems
Department of Computer Science, Kumamoto University, Japan, (March 2001)Abstract
Recently, complex industrial plants such as mobile
robots, flexible manufacturing system etc., are often
required to perform complex tasks with high precision
under ill-defined conditions, and conventional control
techniques may not be quite effective in these systems.
Soft computing approaches are some computational models
inspired by the simulated human and/or natural
intelligence, and includes fuzzy logic, artificial
neural networks, genetic and evolutionary algorithms.
There have been many successful researches for the
identification and control of nonlinear systems by
using various soft computing techniques with different
computational architectures. The experiences gained
over the past decade indicate that it can be more
effective to use the various soft computing approaches
in a combined manner. But there is no common
recognition about how to combine them in an effective
way, and a unified framework of hybrid soft computing
models in which various soft computing models can be
developed, evolved and evaluated has not been
established.