Development Multiple Neuro-Fuzzy System Using Back-propagation Algorithm

Abstract

When fuzzy systems are highly nonlinear or include a large number of input variables, the number of fuzzy rules constituting the underlying model is usually large. Dealing with a large-size fuzzy model may face many practical problems in terms of training time, ease of updating, generalizing ability and interpretability. Multiple Fuzzy System (MFS) is one of effective methods to reduce the number of rules, increase the speed to obtain good results. This paper is therefore proposes another approach call Multiple Neuro-Fuzzy System (MNFS) which can further enhance the performance of the MFS approach. The new approach is used Back-propagation algorithm in the learning process. The performance of the proposed approach evaluates and compares with MFS by three experiments on nonlinear functions. Simulation results demonstrate the effectiveness of the new approach than MFS with regards to enhancement of the accuracy of the results.