Completely Supervised Training Algorithms for B-spline Neural Networks and Neuro-fuzzy Systems

Abstract

ABSTRACT Complete supervised training algorithms tor B-spline neural networks and fuzzy rule-based systems are discussed. By introducing the relationships between B-spline neural networks and certain types of fuzzy models, training algorithms developed initially for neural networks can be adapted to fuzzy systems. The standard training criterion is reformulated, by separating its parameters into linear and non linear ones. By employing this reformulation with the Levenberg-Marquardt algorithm, a new training method. offering a fast rate or convergence, and a robust performance compared with the standard Error-Back Propagation algorithm.