On Learning Rates in a Class of Neuro-Fuzzy Learning Algorithms

Abstract

In this paper, we study the problem of designing of learning rates in a class of neuro-fuzzy learning algorithms, which are widely used in recent fuzzy applications for tuning fuzzy inference rules. The analysis on the convergence of the learning algorithm in the cases of tuning parameters of fuzzy rules is conducted. Sufficient conditions for choosing appropriate learning rates have been presented such that an optimal fuzzy system model can be constructed.