Neurofuzzy Adaptive Modelling and Control (Prentice-Hall International Series in Systems and Control Engineering)

The drive for autonomy in manufacturing is making increasing demands on control systems, both for improved performance and extra flexibility. Traditional control systems generally make infeasible assumptions which limit their application, therefore current research has concentrated on intelligent control techniques in order to make systems flexible and robust. This book provides a unified description of several adaptive neural and fuzzy networks and introduces the associate memory class of systems, which describe the similarities and differences existing between fuzzy and neural algorithms. Three networks are desctibed in detail - the Albus CMAC, the B-spline network and a class of fuzzy systems - and then analyzed, their desirable features (local learning, linearly dependent on the parameter set, fuzzy interpretation) are emphasized and the algorithms are all evaluated on a common time series prediction problem.

This text aims to provide a unified treatment of neurofuzzy learning systems. It investigates the theory behind adaptive neurofuzzy systems, and compares and contrasts several neurofuzzy networks.