GMBH-type neural network modeling in evolutionary optimization

Abstract

We discuss a new design of group method of data handling (GMDH)-type neural network using evolutionary algorithm. The performances of the GMDH-type network depend strongly on the number of input variables and order of the polynomials to each node. They must be fixed by designer in advance before the architecture is constructed. So the trial and error method must go with heavy computation burden and low efficiency. To alleviate these problems we employed evolutionary algorithms. The order of the polynomial, the number of input variables, and the optimum input variables are encoded as a chromosome and fitness of each chromosome is computed. The appropriate information of each node are evolved accordingly and tuned gradually throughout the GA iterations. By the simulation results, we can show that the proposed networks have good performance. © Springer-Verlag Berlin Heidelberg 2005.