Evolving neural network CMAC and its applications

Abstract

The conventional neural network (NN) CMAC (Cerebellar Model Articulation Controller) can be applied in many real-world applications thanks to its high learning speed and good generalization capability. In this paper, it is proposed to utilize a neuro-evolutional approach to adjust CMAC parameters and construct mathematical models of nonlinear objects in the presence of the Gaussian noise. The general structure of the evolving NN CMAC (ECMAC) is considered. The paper demonstrates that the evolving NN CMAC can be used effectively for the identification of nonlinear dynamical systems. The simulation of the proposed approach for various nonlinear objects is performed. The results proved the effectiveness of the developed methods.