ART-type CMAC network classifier

Authors:
Ming-Feng Yeh;Min-Shyang Leu
Affiliations:
Department of Electrical Engineering, Lunghwa University of Science and Technology, Taoyuan 33327, Taiwan;Department of Electrical Engineering, Lunghwa University of Science and Technology, Taoyuan 33327, Taiwan
Venue:
Neurocomputing
Year:
2011

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Abstract

In this paper, the learning process of ART 2 (adaptive resonant theory) network is applied to construct the structure of cerebellar model articulation controller (CMAC) to form an ART-type CMAC network. The proposed updating rule is in an unsupervised manner as the ART 2 network or the self-organizing map (SOM), and could equally distribute the learning information into the association memory locations as the CMAC network. If the winner fails a vigilance test, a new state is created; otherwise, the memory contents corresponding to the winner are updated according to the learning information. Like SOM, the proposed network also has a neighborhood region, but the neighborhood region is implicit in the network structure and need not be defined in advance. This paper also analyzes the convergence properties of the ART-type CMAC network. The proposed network is applied to solve data classification problems for illustration. Experiment results demonstrate the effectiveness and feasibility of the ART-type CMAC network in solving five benchmark datasets selected from the UCI repository.