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Iterative genetic algorithm for learning efficient fuzzy rule set

Published online by Cambridge University Press:  01 November 2003

MENG HIOT LIM  and
WILLIE NG
MENG HIOT LIM
Affiliation:
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore
WILLIE NG
Affiliation:
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore
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Abstract

We present a methodology of learning fuzzy rules using an iterative genetic algorithm (GA). The approach incorporates a scheme of partitioning the entire solution space into individual subspaces. It then employs a mechanism to progressively relax or tighten the constraint. The relaxation or tightening of constraint guides the GA to the subspace for further iteration. The system referred to as the iterative GA learning module is useful for learning an efficient fuzzy control algorithm based on a predefined linguistic terms set. The overall approach was applied to learn a fuzzy algorithm for a water bath temperature control. The simulation results demonstrate the effectiveness of the approach in automating an industrial process.

Keywords

Fuzzy RulesGenetic LearningIterative Genetic AlgorithmPlant AutomationProcess Control
Type
Research Article
Information
AI EDAM , Volume 17 , Issue 4 , November 2003 , pp. 335 - 347
Copyright
2003 Cambridge University Press

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References

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