A hierarchical CMAC architecture for context dependent function approximation

Abstract

A hierarchical cerebellar model articulation controller (CMAC) architecture suitable for context-dependent function approximation is proposed. The objective is to approximate several distinct nonlinear functions, one for each of several contexts. The active context is determined from the values of context variables, and smooth interpolation between different contexts is possible. The learning algorithms used can be similar to those of the hierarchical mixtures of experts (HME) as CMAC networks are linear in parameters. The proposed architecture converges quickly and has very low computational requirements when first order learning algorithms are used. The effectiveness of the architecture is demonstrated on a composite nonlinear regression task involving three Gaussian functions.