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Training neuro-fuzzy boiler identifier with genetic algorithm and error back-propagation

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2 Author(s)
H. Ghezelayagh ; Dept. of Electr. Eng., Pennsylvania State Univ., University Park, PA, USA ; K. Y. Lee

A multi-layer neuro-fuzzy system presents identification of a drum type boiler. This identification provides a rule-based approach to approximate the boiler dynamics. The interconnections of neuro-fuzzy layers furnish these fuzzy rules. A genetic algorithm (GA) trains the neuro-fuzzy identifier and extracts the linguistic fuzzy rules from measured boiler data. This GA training takes the advantages of nonbinary alphabet and compound chromosomes to train the neuro-fuzzy identifier. An error backpropagation training methodology is chosen to tune the membership function parameters. This neuro-fuzzy identifier obtains time response similar to boiler model while it avoids mathematical complexity of model dynamics

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Power Engineering Society Summer Meeting, 1999. IEEE  (Volume:2 )

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