SIRMs Connected Fuzzy Inference Model Applied to Process Control - Automatic Tuning Using a Genetic Algorithm

Carta Cavalcante Koike; Kaoru Hirota

doi:10.20965/jaciii.1999.p0299

SIRMs Connected Fuzzy Inference Model Applied to Process Control - Automatic Tuning Using a Genetic Algorithm

Carta Cavalcante Koike^*, Kaoru Hirota

^*Corresponding author for this work

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

Abstract

A tuning process proposed for the single input rule modules (SIRMs) connected inference model is applied to process control. Using a genetic algorithm (GA), the importance degree is adjusted to improve process response. Simulation using first-order processes with dead time verified the validity of this approach.

Original language	English
Pages (from-to)	299-302
Number of pages	4
Journal	Journal of Advanced Computational Intelligence and Intelligent Informatics
Volume	3
Issue number	4
DOIs	https://doi.org/10.20965/jaciii.1999.p0299
Publication status	Published - Aug 1999
Externally published	Yes

Keywords

Automatic tuning
Fuzzy inference
Genetic algorithm

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10.20965/jaciii.1999.p0299

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abstract = "A tuning process proposed for the single input rule modules (SIRMs) connected inference model is applied to process control. Using a genetic algorithm (GA), the importance degree is adjusted to improve process response. Simulation using first-order processes with dead time verified the validity of this approach.",

keywords = "Automatic tuning, Fuzzy inference, Genetic algorithm",

author = "Koike, {Carta Cavalcante} and Kaoru Hirota",

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AU - Hirota, Kaoru

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PY - 1999/8

Y1 - 1999/8

N2 - A tuning process proposed for the single input rule modules (SIRMs) connected inference model is applied to process control. Using a genetic algorithm (GA), the importance degree is adjusted to improve process response. Simulation using first-order processes with dead time verified the validity of this approach.

AB - A tuning process proposed for the single input rule modules (SIRMs) connected inference model is applied to process control. Using a genetic algorithm (GA), the importance degree is adjusted to improve process response. Simulation using first-order processes with dead time verified the validity of this approach.

KW - Automatic tuning

KW - Fuzzy inference

KW - Genetic algorithm

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M3 - Article

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ER -

SIRMs Connected Fuzzy Inference Model Applied to Process Control - Automatic Tuning Using a Genetic Algorithm

Abstract

Keywords

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