標題: | Supervised adaptive hamming net for classification of multiple-valued patterns |
作者: | Hung, CA Lin, SF 電控工程研究所 Institute of Electrical and Control Engineering |
公開日期: | 1-四月-1997 |
摘要: | A Supervised Adaptive Hamming Net (SAHN) is introduced for incremental learning of recognition categories in response to arbitrary sequences of multiple-valued or binary-valued input patterns. The binary-valued SAHN derived from the Adaptive Hamming Net (AHN) is functionally equivalent to a simplified ARTMAP, which is specifically designed to establish many-to-one mappings. The generalization to learning multiple-valued input patterns is achieved by incorporating multiple-valued logic into the AHN. In this paper, we examine some useful properties of learning in a P-valued SAHN. In particular, an upper bound is derived on the number of epochs required by the P-valued SAHN to learn a list of input-output pairs that is repeatedly presented to the architecture. Furthermore, we connect the P-valued SAHN with the binary-valued SAHN via the thermometer code. |
URI: | http://hdl.handle.net/11536/613 |
ISSN: | 0129-0657 |
期刊: | INTERNATIONAL JOURNAL OF NEURAL SYSTEMS |
Volume: | 8 |
Issue: | 2 |
起始頁: | 181 |
結束頁: | 200 |
顯示於類別: | 期刊論文 |