Self-optimizing for the Structure of CMAC neural network

Abstract

CMAC neural network has been widely applied on the real-time control of the nonlinear systems, such as robot control, aerocraft control and etc. However, the required memory size increases exponentially with the input dimension of CMAC, it may conduct to serious computational challenges in its on-line application. In this paper, experimental protocol is used for illustrating how the structure of CMAC influence the approximation qualities and required memory size. It is found that an optimal structure carrying the minimum modeling error could be achieved. The self-optimizing algorithm is then developed to adjust the structure of CMAC neural network in order to accomplish the minimum modeling error with minimum required memory size, without increase the structure complexness of the network.