DocumentCode
3447738
Title
Generating fuzzy rule-based systems from examples
Author
Chang, Te-Min ; Yih, Yuehwern
Author_Institution
Sch. of Ind. Eng., Purdue Univ., West Lafayette, IN, USA
fYear
1996
fDate
11-14 Dec 1996
Firstpage
37
Lastpage
42
Abstract
This paper proposes a general methodology to generate fuzzy rule-based systems automatically from examples. The objective of this work is to generate fuzzy systems with good mapping ability and generalization ability as well. This methodology consists of five steps. Inductive learning is incorporated to enhance fuzzy system´s generalization ability. Experiments are conducted to evaluate the system performance of generated fuzzy systems based on two sets of data in the literature
Keywords
fuzzy systems; generalisation (artificial intelligence); knowledge based systems; learning by example; fuzzy rule-based systems; generalization; inductive learning; mapping ability; Control engineering; Data mining; Function approximation; Fuzzy set theory; Fuzzy sets; Fuzzy systems; Industrial engineering; Knowledge based systems; System performance; Training data;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Systems Symposium, 1996. Soft Computing in Intelligent Systems and Information Processing., Proceedings of the 1996 Asian
Conference_Location
Kenting
Print_ISBN
0-7803-3687-9
Type
conf
DOI
10.1109/AFSS.1996.583550
Filename
583550
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