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Title: Approximate case-based reasoning on neural networks
Authors: Shen, Z. 
Lui, H.C. 
Ding, L. 
Keywords: Approximate case-based reasoning
approximate reasoning
fuzzy logic
neural networks
Issue Date: Jan-1994
Citation: Shen, Z.,Lui, H.C.,Ding, L. (1994-01). Approximate case-based reasoning on neural networks. International Journal of Approximate Reasoning 10 (1) : 75-98. ScholarBank@NUS Repository.
Abstract: In this paper, based on the deeper analysis of the features of fuzzy logic and approximate reasoning, the concept of approximate case-based reasoning (ACBR) is introduced. According to the inference mechanism of ACBR, an implementation on neural networks is proposed. Mapping the implication relation between the premise(s) and the consequence of a fuzzy rule to the weight of a corresponding neural network unit, an approximate case-based reasoning on neural networks can be realized. The self-organizing and self-learning procedure can be executed by modifying the weight. © 1994.
Source Title: International Journal of Approximate Reasoning
ISSN: 0888613X
Appears in Collections:Staff Publications

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