DocumentCode
2271340
Title
Generating fuzzy rules for a neural fuzzy classifier
Author
Li, Chihwen Chris ; Wu, Chwan-Hwa John
Author_Institution
Dept. of Electr. Eng., Nat. I-Lan Inst. of Agric. & Technol., Taiwan, China
fYear
1994
fDate
26-29 Jun 1994
Firstpage
1719
Abstract
It is difficult to design a classifier when overlap problems occur between the decision regions of different classes. A top-down learning procedure is described which trains a neural fuzzy (NF) system from global to local views of the overlap decision region, and generates nested IF-THEN rules. With these rules, the NF system can correctly separate similar classes within the overlap decision region. Two operational examples of the NF system are given
Keywords
content-addressable storage; fuzzy logic; fuzzy neural nets; hierarchical systems; learning (artificial intelligence); pattern classification; fuzzy associative memory; fuzzy rules generation; global views; local views; nested IF-THEN rules; neural fuzzy classifier; operational examples; overlap decision region; top-down learning procedure; training; Adaptive systems; Agriculture; Associative memory; Data engineering; Fuzzy control; Fuzzy reasoning; Fuzzy sets; Fuzzy systems; Noise measurement; Programmable control;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Systems, 1994. IEEE World Congress on Computational Intelligence., Proceedings of the Third IEEE Conference on
Conference_Location
Orlando, FL
Print_ISBN
0-7803-1896-X
Type
conf
DOI
10.1109/FUZZY.1994.343597
Filename
343597
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