NDLI: An algorithm of extracting fuzzy rules directly from numerical examples by using FNN

Content Provider	IEEE Xplore Digital Library
Author	Lili Rong Zhongtuo Wang
Copyright Year	1996
Description	Author affiliation: Inst. of Syst. Eng., Dalian Univ. of Technol., China (Lili Rong)
Abstract	In this paper, a general method to obtain fuzzy rules directly from numerical data is proposed. The membership function of antecedent part and consequent part is determined by calculating the variance and expectation of the examples. The extraction of fuzzy rules is done by using a fuzzy neural network. The algorithm has two parts. The first part is to determine the optimal number of fuzzy rules. The second part is to improve the accuracy of the inference system. The structure of the FNN is different from previous methods. In the rule layer, there are three counters in each node in order to calculate the maximum degree and the probability of the rule. The output of the node and the weight connecting to the next layer depend on the contents of these counters. From these counters, we can obtain the useful fuzzy rules. The backpropagation learning method is used to improve the accuracy of the system. Finally, through a simulation example, the effectiveness of the proposed algorithm is verified.
Starting Page	1067
Ending Page	1072
File Size	501361
Page Count	6
File Format	PDF
ISBN	0780332806
ISSN	1062922X
DOI	10.1109/ICSMC.1996.571230
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1996-10-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy neural networks Fuzzy control Data mining Counting circuits Fuzzy systems Learning systems Inference algorithms Probability Joining processes Systems engineering and theory
Content Type	Text
Resource Type	Article

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