Fast Perceptron Learning by Fuzzy Controlled Dynamic Adaptation of Network Parameters
Saman Kumara Halgamuge, Andreas Mari, Manfred Glesner · 1994
Application of fuzzy control for obtaining better performance from conventional neural networks is a new area in the field of fuzzy-neural combined systems. Conventional backpropagation algorithm for example can be improved by changing network parameters, based on the empirical knowledge gained by the user. This manual adaptation is effectively replaced by a fuzzy controller that contains the a priori knowledge in form from membership functions and rules. The implemented modified backpropagation algorithm with a fuzzy controller for dynamic adaptation of network parameters is tested with a benchmark data set and two real world problems.