Neighborhood sequential and random training techniques for CMAC

An adaptive control algorithm based on Albus´ CMAC (Cerebellar Model Articulation 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 generate mathematical functions, and both methods successfully circumvented the training interference resulting from CMAC´s inherent generalization property. In the neighborhood sequential training method, a strategy was devised to utilize the discrete, finite state nature of the CMAC´s address space for selecting points in the input space which would train CMAC systems in the most rapid manner possible. The random training method was found to converge on the training function with the greatest precision, although it requires longer training periods than the neighborhood sequential training method to achieve a desired performance level