A Fuzzy Model for Learning and Adaptivity

Robert J. Hammell; Thomas Sudkamp

doi:10.1109/TAI.1997.632301

A Fuzzy Model for Learning and Adaptivity

Robert J. Hammell, Thomas Sudkamp

Wright State University

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

Abstract

Fuzzy models have been designed to represent approximate or imprecise relationships in complex systems and have been successfully employed in control systems, expert systems, and decision analysis. A hierarchical architecture for fuzzy modeling and inference has been developed to learn an initial set of rules from training data and allow adaptation of the rule base via system performance feedback. A general adaptive algorithm is presented and its performance examined for three types of adaptive behavior: continued learning, gradual change, and drastic change. In each of the three types of behavior, 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	Proceedings Ninth IEEE International Conference on Tools with Artificial Intelligence
DOIs	https://doi.org/10.1109/TAI.1997.632301
State	Published - Jan 1 1997

Keywords

Adaptive algorithm
Control system synthesis
Expert systems
Feedback
Fuzzy control
Fuzzy sets
Fuzzy systems
Hybrid intelligent systems
System performance
Training data

Disciplines

Computer Sciences
Engineering
Mathematics
Physical Sciences and Mathematics

Access to Document

10.1109/TAI.1997.632301License: Unspecified

http://ieeexplore.ieee.org/document/632301/

Cite this

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abstract = " Fuzzy models have been designed to represent approximate or imprecise relationships in complex systems and have been successfully employed in control systems, expert systems, and decision analysis. A hierarchical architecture for fuzzy modeling and inference has been developed to learn an initial set of rules from training data and allow adaptation of the rule base via system performance feedback. A general adaptive algorithm is presented and its performance examined for three types of adaptive behavior: continued learning, gradual change, and drastic change. In each of the three types of behavior, 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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AB - Fuzzy models have been designed to represent approximate or imprecise relationships in complex systems and have been successfully employed in control systems, expert systems, and decision analysis. A hierarchical architecture for fuzzy modeling and inference has been developed to learn an initial set of rules from training data and allow adaptation of the rule base via system performance feedback. A general adaptive algorithm is presented and its performance examined for three types of adaptive behavior: continued learning, gradual change, and drastic change. In each of the three types of behavior, 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.

KW - Adaptive algorithm

KW - Control system synthesis

KW - Expert systems

KW - Feedback

KW - Fuzzy control

KW - Fuzzy sets

KW - Fuzzy systems

KW - Hybrid intelligent systems

KW - System performance

KW - Training data

UR - https://corescholar.libraries.wright.edu/cse/442

UR - http://ieeexplore.ieee.org/document/632301/

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DO - 10.1109/TAI.1997.632301

M3 - Article

JO - Proceedings Ninth IEEE International Conference on Tools with Artificial Intelligence

JF - Proceedings Ninth IEEE International Conference on Tools with Artificial Intelligence

ER -

A Fuzzy Model for Learning and Adaptivity

Abstract

Keywords

Disciplines

Access to Document

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Cite this