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JACIII Vol.22 No.2 pp. 172-175
doi: 10.20965/jaciii.2018.p0172
(2018)

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Optimization of Constrained SIRMs Connected Type Fuzzy Inference Model Using Two-Phase Simplex Method

Takeshi Nagata*, Hirosato Seki**, and Hiroaki Ishii***

*Department of Mathematical Sciences, Kwansei Gakuin University
2-1 Gakuen, Sanda, Hyogo 669-1337, Japan

**Graduate School of Engineering Science, Osaka University
1-3 Machikaneyama-cho, Toyonaka, Osaka 560-8531, Japan

***Research Center for Mathematical Sciences, Kwansei Gakuin University
2-1 Gakuen, Sanda, Hyogo 669-1337, Japan

Received:
August 31, 2017
Accepted:
November 2, 2017
Published:
March 20, 2018
Creative Commons License
Keywords:
fuzzy inference systems, Single Input Rule Modules connected fuzzy inference model (SIRMs model), constrained SIRMs model, two-phase simplex method
Abstract

Single Input Rule Modules connected fuzzy inference model (SIRMs model, for short) by Yubazaki et al. can decrease the number of fuzzy rules drastically in comparison with the conventional fuzzy inference models. However, it is difficult to understand the meaning of the weight for the SIRMs model because the value of the weight has no restriction in the learning rules. Therefore, the paper proposes a constrained SIRMs model in which the weights are in [0,1] by using two-phase simplex method. Moreover, it shows that the applicability of the proposed model by applying it to a medical diagnosis.

Cite this article as:
T. Nagata, H. Seki, and H. Ishii, “Optimization of Constrained SIRMs Connected Type Fuzzy Inference Model Using Two-Phase Simplex Method,” J. Adv. Comput. Intell. Intell. Inform., Vol.22 No.2, pp. 172-175, 2018.
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References
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Last updated on Apr. 19, 2024