A hybrid one-by-one learning with incremental learning algorithm in CMAC

A one-by-one learning algorithm similar to traditional incremental learning for CMAC is suggested. The convergence property is investigated based on the principles of geometric sequence and iteration theory of linear equations. The sufficient condition for convergence of the algorithm is the same as that of incremental learning. The performance of two algorithms is compared, then a hybrid one-by-one learning with incremental learning algorithm is proposed. The simulation results of two-dimension function approximation prove that the hybrid algorithm has a better performance in convergent speed and precision.