NEIGHBORHOOD SEQUENTIAL AND RANDOM TRAINING TECHNIQUES FOR CMAC

An adaptive control algorithm based on Albus' CMAC (Cerebellar Model A rticulation Controller) was studied with emphasis on how to train CMAC systems. Two training techniques-neighborhood sequential training and random training, have been devised, These techniques were used to gen erate mathematical functions, and both methods successfully circumvent ed the training interference resulting from CMAC's inherent generaliza tion property, In the neighborhood sequential training method, a strat egy was devised to utilize the discrete, finite state nature of the CM AC's address space for selecting points in the input space which would train CMAC systems in the most rapid manner possible. The random trai ning method was found to converge on the training function with the gr eatest precision, although it requires longer training periods than th e neighborhood sequential training method to achieve a desired perform ance level.