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Journal of Inforamtion Science and Engineering, Vol.13 No.4, pp.534-562 (December 1997)
A Mach-Based Clustering Neural Network for Stable
Category Learning of Multiple-Valued Patterns

Cheng-An Hung and Sheng-Fuu Lin
Department of Control Engineering
National Chiao Tung University
Hsinchu, Taiwan 300, R.O.C.

A match-based clustering neural network (MBCNN) for unsupervised pattern recognition is introduced. MBCNN is an extension of the adaptive Hamming net (AHN), which incorporates multiple-valued logic operations. AHN can only process binary-valued patterns, whereas MBCNN can process either multiple-valued or binary-valued patterns. In this paper, we connect AHN with MBCNN via the thermometer code. In addition, we also present a number of properties related to the learning process in MBCNN. In particular, an upper bound is derived for the number of list presentations required by an MBCNN to learn an arbitrary list of P-valued input patterns that is repeatedly presented to the architecture. Finally, a comparison between MBCNN and fuzzy ART is given.

Keywords: match-based clustering neural network, adaptive Hamming net, multiple-valued logic, thermometer code, fuzzy ART

Received September 24, 1996; revised January 6, 1997.
Communicated by Youn-Long Lin.