A method of constructing fuzzy neural network based on rough set theory

A method of constructing fuzzy neural network structure by using rough set theory is presented . Since rough set theory has strong ability of analyzing numerical value and fuzzy neural network has the ability of approximating function nicely, a neural network model which has good intelligibility, simple computation and fast convergence is constructed by combining both theory. The main process to construct this network is as follows: firstly to acquire rules from present data set by rough set theory; then the cell number of each layer and relevant initial parameters are constructed according to these rules; finally all kinds of parameters are computed by BP(back promulgation) arithmetic and the design of the network is finished. Also in this paper an example of approximating a 2D nonlinear function is discussed and the feasibility and validity of the method are proved.