Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/72670
Title: Hierarchical CMAC architecture for context dependent function approximation
Authors: Tham, Chen-Khong 
Issue Date: 1996
Source: Tham, Chen-Khong (1996). Hierarchical CMAC architecture for context dependent function approximation. IEEE International Conference on Neural Networks - Conference Proceedings 1 : 629-634. ScholarBank@NUS Repository.
Abstract: A hierarchical Cerebellar Model Articulation Controller (CMAC) architecture suitable for context-dependent function approximation is proposed. The objective is to approximate several distinct non-linear 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 non-linear regression task involving three Gaussian functions.
Source Title: IEEE International Conference on Neural Networks - Conference Proceedings
URI: http://scholarbank.nus.edu.sg/handle/10635/72670
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