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

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

1 引用（Scopus）

摘要

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.

源语言	英语
主期刊名	1998 IEEE International Conference on Fuzzy Systems Proceedings - IEEE World Congress on Computational Intelligence
出版商	Institute of Electrical and Electronics Engineers Inc.
页	1277-1280
页数	4
ISBN（印刷版）	078034863X, 9780780348639
DOI	https://doi.org/10.1109/FUZZY.1998.686302
出版状态	已出版 - 1998
已对外发布	是
活动	1998 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 1998 - Anchorage, 美国期限: 4 5月 1998 → 9 5月 1998

丛书

姓名	1998 IEEE International Conference on Fuzzy Systems Proceedings - IEEE World Congress on Computational Intelligence
卷	2

会议

会议	1998 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 1998
国家/地区	美国
市	Anchorage
时期	4/05/98 → 9/05/98

访问文件

10.1109/FUZZY.1998.686302

其它文件与链接

链接到 Scopus 的出版物

引用此

Cavalcante, C., & Hirota, K. (1998). SIRMs connected fuzzy inference model tuning using genetic algorithm. 在 1998 IEEE International Conference on Fuzzy Systems Proceedings - IEEE World Congress on Computational Intelligence (页码 1277-1280). 期刊论文 686302 (1998 IEEE International Conference on Fuzzy Systems Proceedings - IEEE World Congress on Computational Intelligence; 卷 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. 页码 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.",

pages = "1277--1280",

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. 在 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, 卷 2, Institute of Electrical and Electronics Engineers Inc., 页码 1277-1280, 1998 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 1998, Anchorage, 美国, 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. 页码 1277-1280 686302 (1998 IEEE International Conference on Fuzzy Systems Proceedings - IEEE World Congress on Computational Intelligence; 卷 2).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - SIRMs connected fuzzy inference model tuning using genetic algorithm

AU - Cavalcante, Carla

AU - Hirota, Kaoru

PY - 1998

Y1 - 1998

N2 - 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.

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

SP - 1277

EP - 1280

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

PB - Institute of Electrical and Electronics Engineers Inc.

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

Y2 - 4 May 1998 through 9 May 1998

ER -

Cavalcante C, Hirota K. SIRMs connected fuzzy inference model tuning using genetic algorithm. 在 1998 IEEE International Conference on Fuzzy Systems Proceedings - IEEE World Congress on Computational Intelligence. Institute of Electrical and Electronics Engineers Inc. 1998. 页码 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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