A hybrid maximum error algorithm with neighborhood training for CMAC

Abstract

Several possible algorithms and training methods for the CMAC network are analyzed thoroughly. Improvements are then examined and a hybrid approach has been developed for the maximum error algorithm by using the neighborhood training technique for the initial training period. The employment of the technique yielded faster initial convergence which is very important for many control applications. The proposed hybrid approach is demonstrated on an inverse kinematics problem of a two-link robot arm.