Evolutionary Strategies for Fuzzy Models: Local vs Global Construction

Thomas Sudkamp; Daniel Spiegel

doi:10.1109/NAFIPS.1999.781683

Evolutionary Strategies for Fuzzy Models: Local vs Global Construction

Thomas Sudkamp, Daniel Spiegel

Wright State University

Research output: Contribution to journal › Article › peer-review

Abstract

This paper presents a framework for studying the effectiveness of evolutionary strategies for generating fuzzy rule bases from training data. The fitness measure needed for selection is obtained by a comparison of the training data with the function approximation defined by a fuzzy rule base. The properties of employing both global and local fitness measures are examined. Rule base completion is obtained by incorporating a global evaluation of the smoothness of the transitions between local regions into the selection process.

Original language	American English
Journal	18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS
DOIs	https://doi.org/10.1109/NAFIPS.1999.781683
State	Published - Jan 1 1999

Keywords

Algorithm design and analysis
Clustering algorithms
Computer science
Data analysis
Fuzzy sets
Marine vehicles
Quantization
Takagi-Sugeno-Kang model
Training data

Disciplines

Computer Sciences
Engineering
Mathematics
Physical Sciences and Mathematics

Access to Document

10.1109/NAFIPS.1999.781683License: Unspecified

http://ieeexplore.ieee.org/document/781683/

Cite this

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abstract = " This paper presents a framework for studying the effectiveness of evolutionary strategies for generating fuzzy rule bases from training data. The fitness measure needed for selection is obtained by a comparison of the training data with the function approximation defined by a fuzzy rule base. The properties of employing both global and local fitness measures are examined. Rule base completion is obtained by incorporating a global evaluation of the smoothness of the transitions between local regions into the selection process.",

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KW - Algorithm design and analysis

KW - Clustering algorithms

KW - Computer science

KW - Data analysis

KW - Fuzzy sets

KW - Marine vehicles

KW - Quantization

KW - Takagi-Sugeno-Kang model

KW - Training data

UR - https://corescholar.libraries.wright.edu/cse/426

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JO - 18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS

JF - 18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS

ER -

Evolutionary Strategies for Fuzzy Models: Local vs Global Construction

Abstract

Keywords

Disciplines

Access to Document

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Cite this