Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/111142
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
URI: http://scholarbank.nus.edu.sg/handle/10635/111142
ISSN: 0888613X
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

72
checked on Jun 21, 2019

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.