NDLI: Extracting fuzzy rules from neural network in connectionist expert system

Content Provider	IEEE Xplore Digital Library
Author	Liang Jing Nezu, K. Xu Jianu
Copyright Year	1996
Description	Author affiliation: University of Science and Technology (Liang Jing)
Abstract	Connectionist expert system has drawn more and more interest because of its adaptability, high speed and robustness. In an ideal connectionist expert both the neural network and the conventional knowledge base should be used as knowledge storage units. Also these two kinds of knowledge have to be transferred to each other conveniently to acquire a high performance. So it remains an important but difficult question to extract explicit rules from weighted connections of a trained network. This paper presented a novel automatic rule extraction method based on BP network. The viability of this approach is demonstrated by a practical example. This simple and effective method is useful not only for connectionist expert system but also for the processing of experiment data, and it also provides a potential way for machine learning.
Starting Page	1383
Ending Page	1387
File Size	428885
Page Count	5
File Format	PDF
ISBN	0780337514
DOI	10.1109/SICE.1996.865471
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1996-07-24
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hybrid intelligent systems Fuzzy neural networks Fuzzy systems Neural networks Intelligent networks Knowledge based systems Machine learning Robustness Knowledge acquisition Knowledge representation
Content Type	Text
Resource Type	Article

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