A genetic based fuzzy-neural networks design for system identification

In this paper, we use a modified Genetic Algorithm (MGA) to construct a fuzzy neural network (FNN), spontaneously, which can approximate a nonlinear function as well as possible. With the specific structure of the chromosome, the special mutation operation and the adequate fitness function, the proposed method with MGA produces a FNN with minimum structure of neural network, smaller number of rules, suitable placement of the premise's fuzzy sets and proper location of the consequent singletons. Finally, an example is illustrated to show the effectiveness of the proposed method on the nonlinear function approximation.