Tuning Membership Functions in Local Evolutionary Learning of Fuzzy Rule Bases

D. Spiegel; T. Sudkamp

doi:10.1109/NAFIPS.2002.1018106

Tuning Membership Functions in Local Evolutionary Learning of Fuzzy Rule Bases

D. Spiegel, T. Sudkamp

Computer Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

The local evolutionary generation of fuzzy rule bases employs independent searches in local regions throughout the input space and combines the local results to produce a global model. The paper presents a rule base tuning strategy that is compatible with the local evolutionary generation of fuzzy rule bases. Rule base tuning is accomplished by modifying the decomposition of the input domain based on the distribution and values of the training data. A local tuning algorithm must maintain a correspondence between competing rules in the population. An experimental suite has been developed to exhibit the potential for model optimization using rule base tuning. of particular interest is the ability of rule base tuning to compensate for the effects of sparse data.

Original language	American English
Title of host publication	2002 Annual Meeting of the North American Fuzzy Information Processing Society, Proceedings - NAFIPS-FLINT 2002
Editors	Olfa Nasraoui, Jim Keller
Publisher	IEEE
Pages	475-480
Number of pages	6
ISBN (Print)	0-7803-7461-4
DOIs	https://doi.org/10.1109/NAFIPS.2002.1018106
State	Published - 2002
Event	Annual Meeting of the North American Fuzzy Information Processing Society, NAFIPS-FLINT 2002 - New Orleans, United States Duration: Jun 27 2002 → Jun 29 2002

Conference

Conference	Annual Meeting of the North American Fuzzy Information Processing Society, NAFIPS-FLINT 2002
Country/Territory	United States
City	New Orleans
Period	6/27/02 → 6/29/02

ASJC Scopus Subject Areas

General Computer Science
General Mathematics

Keywords

Algorithm design and analysis
Computer science
Data analysis
Evolutionary computation
Fuzzy sets
Genetic mutations
Training data

Disciplines

Computer Sciences
Engineering
Mathematics

Access to Document

10.1109/NAFIPS.2002.1018106License: Unspecified

Cite this

Tuning Membership Functions in Local Evolutionary Learning of Fuzzy Rule Bases. / Spiegel, D.; Sudkamp, T.
2002 Annual Meeting of the North American Fuzzy Information Processing Society, Proceedings - NAFIPS-FLINT 2002. ed. / Olfa Nasraoui; Jim Keller. IEEE, 2002. p. 475-480 1018106.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Spiegel, D & Sudkamp, T 2002, Tuning Membership Functions in Local Evolutionary Learning of Fuzzy Rule Bases. in O Nasraoui & J Keller (eds), 2002 Annual Meeting of the North American Fuzzy Information Processing Society, Proceedings - NAFIPS-FLINT 2002., 1018106, IEEE, pp. 475-480, Annual Meeting of the North American Fuzzy Information Processing Society, NAFIPS-FLINT 2002, New Orleans, United States, 6/27/02. https://doi.org/10.1109/NAFIPS.2002.1018106

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abstract = " The local evolutionary generation of fuzzy rule bases employs independent searches in local regions throughout the input space and combines the local results to produce a global model. The paper presents a rule base tuning strategy that is compatible with the local evolutionary generation of fuzzy rule bases. Rule base tuning is accomplished by modifying the decomposition of the input domain based on the distribution and values of the training data. A local tuning algorithm must maintain a correspondence between competing rules in the population. An experimental suite has been developed to exhibit the potential for model optimization using rule base tuning. of particular interest is the ability of rule base tuning to compensate for the effects of sparse data.",

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AB - The local evolutionary generation of fuzzy rule bases employs independent searches in local regions throughout the input space and combines the local results to produce a global model. The paper presents a rule base tuning strategy that is compatible with the local evolutionary generation of fuzzy rule bases. Rule base tuning is accomplished by modifying the decomposition of the input domain based on the distribution and values of the training data. A local tuning algorithm must maintain a correspondence between competing rules in the population. An experimental suite has been developed to exhibit the potential for model optimization using rule base tuning. of particular interest is the ability of rule base tuning to compensate for the effects of sparse data.

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Tuning Membership Functions in Local Evolutionary Learning of Fuzzy Rule Bases

Abstract

Conference

ASJC Scopus Subject Areas

Keywords

Disciplines

Access to Document

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