SIRMs Connected Fuzzy Inference Model Applied to Process Control — Automatic Tuning Using a Genetic Algorithm Carla Cavalcante

Koike; Kaoru Hirota

doi:10.20965/jaciii.1999.p0299

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JACIII Vol.3 No.4 pp. 299-302

doi: 10.20965/jaciii.1999.p0299

(1999)

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SIRMs Connected Fuzzy Inference Model Applied to Process Control — Automatic Tuning Using a Genetic Algorithm Carla Cavalcante

Koike^* and Kaoru Hirota^**

^*Departamento de Engenharia Mecanica e Engenharia, Universidadl de Brasilia

^**Department of Computational Intelligence and Systems Science Interdisciplinary Graduate School of Science and Engineering Tokyo Institute of Technology

Received:

November 20, 1998

Accepted:

March 4, 1999

Published:

August 20, 1999

Keywords:

Fuzzy inference, Genetic algorithm, Automatic tuning

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.

Cite this article as:

Koike and Kaoru Hirota, “SIRMs Connected Fuzzy Inference Model Applied to Process Control — Automatic Tuning Using a Genetic Algorithm Carla Cavalcante,” J. Adv. Comput. Intell. Intell. Inform., Vol.3, No.4, pp. 299-302, 1999.

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SIRMs Connected Fuzzy Inference Model Applied to Process Control — Automatic Tuning Using a Genetic Algorithm Carla Cavalcante

Koike* and Kaoru Hirota**

Koike^* and Kaoru Hirota^**