SIRMs connected fuzzy inference model tuning using genetic algorithm

Carla Cavalcante; Kaoru Hirota

doi:10.1109/FUZZY.1998.686302

SIRMs connected fuzzy inference model tuning using genetic algorithm

Carla Cavalcante
, Kaoru Hirota

Institute of Science Tokyo

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

The single input rule modules (SIRMs) connected inference model is a fuzzy inference model in which a single input rule module is constructed for each system input variable. The output of the module is weighted by the degree of importance for each input and then summarized it into the system output. A tuning algorithm for this model applied to function recognition is suggested based on the steepest descent method. However, the number of rules can not be optimized. In this work, a tuning process based on the genetic algorithm is proposed. It allows a wide search for tuned parameters with optimized number of rules. A nonlinear function recognition simulation experiment is done to confirm the validity of the proposed method.

Original language	English
Title of host publication	1998 IEEE International Conference on Fuzzy Systems Proceedings - IEEE World Congress on Computational Intelligence
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1277-1280
Number of pages	4
ISBN (Print)	078034863X, 9780780348639
DOIs	https://doi.org/10.1109/FUZZY.1998.686302
Publication status	Published - 1998
Externally published	Yes
Event	1998 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 1998 - Anchorage, United States Duration: 4 May 1998 → 9 May 1998

Publication series

Name	1998 IEEE International Conference on Fuzzy Systems Proceedings - IEEE World Congress on Computational Intelligence
Volume	2

Conference

Conference	1998 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 1998
Country/Territory	United States
City	Anchorage
Period	4/05/98 → 9/05/98

Access to Document

10.1109/FUZZY.1998.686302

Cite this

Cavalcante, C., & Hirota, K. (1998). SIRMs connected fuzzy inference model tuning using genetic algorithm. In 1998 IEEE International Conference on Fuzzy Systems Proceedings - IEEE World Congress on Computational Intelligence (pp. 1277-1280). Article 686302 (1998 IEEE International Conference on Fuzzy Systems Proceedings - IEEE World Congress on Computational Intelligence; Vol. 2). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/FUZZY.1998.686302

Cavalcante, Carla ; Hirota, Kaoru. / SIRMs connected fuzzy inference model tuning using genetic algorithm. 1998 IEEE International Conference on Fuzzy Systems Proceedings - IEEE World Congress on Computational Intelligence. Institute of Electrical and Electronics Engineers Inc., 1998. pp. 1277-1280 (1998 IEEE International Conference on Fuzzy Systems Proceedings - IEEE World Congress on Computational Intelligence).

@inproceedings{fc2c55a4b4934abeace1c276870d21b6,

title = "SIRMs connected fuzzy inference model tuning using genetic algorithm",

abstract = "The single input rule modules (SIRMs) connected inference model is a fuzzy inference model in which a single input rule module is constructed for each system input variable. The output of the module is weighted by the degree of importance for each input and then summarized it into the system output. A tuning algorithm for this model applied to function recognition is suggested based on the steepest descent method. However, the number of rules can not be optimized. In this work, a tuning process based on the genetic algorithm is proposed. It allows a wide search for tuned parameters with optimized number of rules. A nonlinear function recognition simulation experiment is done to confirm the validity of the proposed method.",

author = "Carla Cavalcante and Kaoru Hirota",

note = "Publisher Copyright: {\textcopyright} 1998 IEEE.; 1998 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 1998 ; Conference date: 04-05-1998 Through 09-05-1998",

year = "1998",

doi = "10.1109/FUZZY.1998.686302",

language = "English",

isbn = "078034863X",

series = "1998 IEEE International Conference on Fuzzy Systems Proceedings - IEEE World Congress on Computational Intelligence",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "1998 IEEE International Conference on Fuzzy Systems Proceedings - IEEE World Congress on Computational Intelligence",

address = "United States",

}

Cavalcante, C & Hirota, K 1998, SIRMs connected fuzzy inference model tuning using genetic algorithm. in 1998 IEEE International Conference on Fuzzy Systems Proceedings - IEEE World Congress on Computational Intelligence., 686302, 1998 IEEE International Conference on Fuzzy Systems Proceedings - IEEE World Congress on Computational Intelligence, vol. 2, Institute of Electrical and Electronics Engineers Inc., pp. 1277-1280, 1998 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 1998, Anchorage, United States, 4/05/98. https://doi.org/10.1109/FUZZY.1998.686302

SIRMs connected fuzzy inference model tuning using genetic algorithm. / Cavalcante, Carla; Hirota, Kaoru.
1998 IEEE International Conference on Fuzzy Systems Proceedings - IEEE World Congress on Computational Intelligence. Institute of Electrical and Electronics Engineers Inc., 1998. p. 1277-1280 686302 (1998 IEEE International Conference on Fuzzy Systems Proceedings - IEEE World Congress on Computational Intelligence; Vol. 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - The single input rule modules (SIRMs) connected inference model is a fuzzy inference model in which a single input rule module is constructed for each system input variable. The output of the module is weighted by the degree of importance for each input and then summarized it into the system output. A tuning algorithm for this model applied to function recognition is suggested based on the steepest descent method. However, the number of rules can not be optimized. In this work, a tuning process based on the genetic algorithm is proposed. It allows a wide search for tuned parameters with optimized number of rules. A nonlinear function recognition simulation experiment is done to confirm the validity of the proposed method.

UR - https://www.scopus.com/pages/publications/0031640633

U2 - 10.1109/FUZZY.1998.686302

DO - 10.1109/FUZZY.1998.686302

M3 - Conference contribution

AN - SCOPUS:0031640633

SN - 078034863X

SN - 9780780348639

T3 - 1998 IEEE International Conference on Fuzzy Systems Proceedings - IEEE World Congress on Computational Intelligence

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EP - 1280

BT - 1998 IEEE International Conference on Fuzzy Systems Proceedings - IEEE World Congress on Computational Intelligence

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T2 - 1998 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 1998

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ER -

Cavalcante C, Hirota K. SIRMs connected fuzzy inference model tuning using genetic algorithm. In 1998 IEEE International Conference on Fuzzy Systems Proceedings - IEEE World Congress on Computational Intelligence. Institute of Electrical and Electronics Engineers Inc. 1998. p. 1277-1280. 686302. (1998 IEEE International Conference on Fuzzy Systems Proceedings - IEEE World Congress on Computational Intelligence). doi: 10.1109/FUZZY.1998.686302

SIRMs connected fuzzy inference model tuning using genetic algorithm

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