Adaptivity in a Hierarchical Fuzzy Model

Robert J. Hammell; Thomas Sudkamp

doi:10.1109/ISUMA.1995.527685

Adaptivity in a Hierarchical Fuzzy Model

Robert J. Hammell, Thomas Sudkamp

Wright State University

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

Abstract

A hierarchical architecture for fuzzy modeling and inference has been developed to allow adaptation based on system performance feedback. A general adaptive algorithm is presented and its performance examined for three types of adaptive behaviour: continued learning, gradual change, and drastic change. In continued learning, the underlying system does not change and the adaptive algorithm utilizes the real time data and associated feedback to improve the accuracy of the existing model. Gradual and drastic change represent fundamental alterations to the system being modeled. In each of the three types of behaviour, the adaptive algorithm has been shown to be able to reconfigure the rule bases to either improve the original approximation or adapt to the new system.

Original language	American English
Journal	Third International Symposium on Uncertainty Modeling and Analysis, 1995, and Annual Conference of the North American Fuzzy Information Processing Society. Proceedings of ISUMA - NAFIPS '95
DOIs	https://doi.org/10.1109/ISUMA.1995.527685
State	Published - Sep 1 1995

Keywords

Adaptive algorithm
Adaptive systems
Artificial intelligence
Associative memory
Computer architecture
Computer science
Feedback
Fuzzy sets
Fuzzy systems
Real time systems

Disciplines

Computer Sciences
Engineering
Mathematics
Physical Sciences and Mathematics

Access to Document

10.1109/ISUMA.1995.527685License: Unspecified

http://ieeexplore.ieee.org/document/527685/

Cite this

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AB - A hierarchical architecture for fuzzy modeling and inference has been developed to allow adaptation based on system performance feedback. A general adaptive algorithm is presented and its performance examined for three types of adaptive behaviour: continued learning, gradual change, and drastic change. In continued learning, the underlying system does not change and the adaptive algorithm utilizes the real time data and associated feedback to improve the accuracy of the existing model. Gradual and drastic change represent fundamental alterations to the system being modeled. In each of the three types of behaviour, the adaptive algorithm has been shown to be able to reconfigure the rule bases to either improve the original approximation or adapt to the new system.

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KW - Adaptive systems

KW - Artificial intelligence

KW - Associative memory

KW - Computer architecture

KW - Computer science

KW - Feedback

KW - Fuzzy sets

KW - Fuzzy systems

KW - Real time systems

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M3 - Article

JO - Third International Symposium on Uncertainty Modeling and Analysis, 1995, and Annual Conference of the North American Fuzzy Information Processing Society. Proceedings of ISUMA - NAFIPS '95

JF - Third International Symposium on Uncertainty Modeling and Analysis, 1995, and Annual Conference of the North American Fuzzy Information Processing Society. Proceedings of ISUMA - NAFIPS '95

ER -

Adaptivity in a Hierarchical Fuzzy Model

Abstract

Keywords

Disciplines

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