Optimizing structural parameters for CMAC (Cerebellar Model Articulation Controller) neural network

Aim. To our knowledge, there do not exist any papers in the open literature on optimizing structural parameters in order to reduce memory size and save training time. We now present our results on such an optimization study. In the full paper, we explain in some detail our research results; in this abstract, we just add some pertinent remarks to naming the first two sections of the full paper. Section 1 is: CMAC neural network structure. Section 2 is: CMAC neural network structural parameters and some function approximation problems. In subsection 2.1, we study the two structural parameters: Step-length quantization and generalization. Then we discuss how the two parameters influence the approximation quality of the CMAC neural network. In subsection 2.2 we study some function approximation problems and error measurements. In sub-subsection 2.2.1 we give two function approximation examples. In sub-subsection 2.2.2, we calculate the function approximation errors of measurements. Finally we perform computer simulations, whose results are given in Tables 1 through 3 and Figs. 6 and 7. These results show preliminarily that our optimization method can not only much decrease memory size but also save training time.