Integration of CMAC and radial basis function techniques

Abstract

In this paper, we integrate the techniques of radial basis functions (RBF) and CMAC to develop a more efficient scheme. Both the CMAC and RBF use the basis functions that may have significant values only in a local input space. CMAC uses the plateau basis while the RBF often uses the Gaussian. Merits of RBF include the continuity and differentiability of the approximate function, and the better accuracy. CMAC has however very attractive convergence properties. In this study, we combine these two techniques with an intention to take the merits from both.