1. INTRODUCTION
Fuzzy systems have been proved to be very useful in control, pattern recognition, signal processing and nonlinear system modeling. Their usage has become popular in soft computing area during the recent years because of their similarity to human reasoning. There are several modeling methods that have been proposed for fuzzy neural networks in recent years [1]–[5] such as mamdani model, simplification reasoning, input function reasoning and TSK, that are being used in many of those networks like adaptive network-based fuzzy inference systems (ANFIS) and radial basis function neural networks (RBFFNN) and are trying to find a way for training strategy and achieving better results. Radial Basis Function Fuzzy Neural Networks (RBFFNN) have three parameters to be trained that are the centers, the standard deviation and the weights that are the values of the output membership functions. They are a kind of fuzzy neural network because if the density of input membership functions is more around one, the output membership function will be spread more around one.