GREY RELATIONAL ANALYSIS BASED APPROACH FOR CMAC LEARNING

Fast Learning and accurate convemence are the two issues to be most concerned in the research area of a Cerebellar Model Articulation Controller (CMAC). This paper investigates to incorporate grey relational analysis with number of training iterations to obtain. an adaptive and appropriate learning rate for each input state to improve the CMAC stability and convergence. Additionally, this paper also proposes that the amount of weight, adjustment to a memory cell of an addressed hyper cube must be relational to the trained input area, grey relational grade in the current training iteration and the inverse of the number of learning times to minimize the learning interference. A credit apportionment approach is thus derived for implementing this idea to achieve fast and accurate learning performance. The results of the experiments conducted in this study clearly demonstrate that the proposed approach provides a, more accurate learning mechanism and faster convergence.