A new learning algorithm for a forecasting neuro-fuzzy network

Abstract

The article addresses the problem of adaptive learning in a neuro-fuzzy network based on Sugeno-type fuzzy inference. A new learning algorithm for tuning of both the antecedent and consequent parts of the fuzzy rules is proposed. The algorithm is derived from the Hartley and Marquardt methods. A characteristic feature of the proposed algorithm is that it does not include time-consuming matrix inversion operations. Simulation results prove the high performance of the algorithm and illustrate its application to time series forecasting.