Optimization of Constrained SIRMs Connected Type Fuzzy Inference Model Using Two-Phase Simplex Method
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Published:2018-03-20
Issue:2
Volume:22
Page:172-175
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ISSN:1883-8014
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Container-title:Journal of Advanced Computational Intelligence and Intelligent Informatics
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language:en
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Short-container-title:JACIII
Author:
Nagata Takeshi,Seki Hirosato,Ishii Hiroaki, , ,
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.
Publisher
Fuji Technology Press Ltd.
Subject
Artificial Intelligence,Computer Vision and Pattern Recognition,Human-Computer Interaction
Reference8 articles.
1. N. Yubazaki, J. Yi, M. Otani, and K. Hirota, “SIRMs dynamically connected fuzzy inference model and its applications,” Proc. IFSA’97, Vol.3, pp. 410-415, Prague, Czech, 1997. 2. J. Yi, N. Yubazaki, and K. Hirota, “Upswing and stabilization control of inverted pendulum and cart system by the SIRMs dynamically connected fuzzy inference model,” Proc. 1999 IEEE Int. Conf. on Fuzzy Syst., Vol.1, pp. 400-405, Seoul, Korea, 1999. 3. J. Yi, N. Yubazaki, and K. Hirota, “A proposal of SIRMs dynamically connected fuzzy inference model for plural input fuzzy control,” Fuzzy Sets Syst., Vol.125, No.1, pp. 79-92, Hawaii, USA, 2002. 4. J. Yi, N. Yubazaki, and K. Hirota, “A new fuzzy controller for stabilization of parallel-type double inverted pendulum system,” Fuzzy Sets Syst., Vol.126, No.1, pp. 105-119, 2002. 5. H. Seki, H. Ishii, and M. Mizumoto, “On the generalization of single input rule modules connected type fuzzy reasoning method,” IEEE Trans. Fuzzy Syst., Vol.16, No.5, pp. 1180-1187, 2008.
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