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|Title:||Approximate case-based reasoning on neural networks||Authors:||Shen, Z.
|Keywords:||Approximate case-based reasoning
|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||URI:||http://scholarbank.nus.edu.sg/handle/10635/111142||ISSN:||0888613X|
|Appears in Collections:||Staff Publications|
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