PROPERTY ANALYSIS OF HYPERBALL CMAC AND MULTIPLE CMAC STRUCTURE

It is a problem to select network parameters of conventional CMAC. Generalization mean squared error(GMSE) and learning mean squared error (LMSE) are used to evaluate generalization ability and learning accuracy of hyperball CMAC. Weight adjusting ratio is introduced and investigated for discovering the relationship between the network parameters and learning property. Research results indicate that generalization ability and learning accuracy are the non decreasing function of weight adjusting ratio. Therefore, the weight adjusting ratio of hyperball CMAC is chosen as large as possible with proper memory space and learning speed. A parallel CMAC structure is also designed for improving nonlinear approximation capabilities of the single hyperball CMAC. Simulation results demonstrate that the proposed strategies are very effective.