Knowledge Acquisition with Deep Fuzzy Inference Model and Its Application to a Medical Diagnosis

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.

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A Learning Method of Non-Differential SIC Fuzzy Inference Model Using Genetic Algorithm and Its Application to a Medical Diagnosis

2019 IEEE International Conference on Systems, Man and Cybernetics (SMC) ◽

10.1109/smc.2019.8914619 ◽

2019 ◽

Author(s):

Genki Ohashi ◽

Tomoki Shimizu ◽

Hirosato Seki ◽

Masahiro Inuiguchi

Keyword(s):

Genetic Algorithm ◽

Medical Diagnosis ◽

Fuzzy Inference ◽

Learning Method ◽

Inference Model

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On the Learning Method, Properties of the Extended Functional-Type SIRMs Connected Fuzzy Inference Model and Their Application to a Medical Diagnosis System

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2018.p0176 ◽

2018 ◽

Vol 22 (2) ◽

pp. 176-183

Author(s):

Diederik van Krieken ◽

Hirosato Seki ◽

Masahiro Inuiguchi ◽

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Keyword(s):

Medical Diagnosis ◽

Fuzzy Inference ◽

Learning Method ◽

Functional Type ◽

Inference Model ◽

Diagnosis System ◽

Single Input ◽

Medical Diagnosis System

Seki et al. have proposed the functional type single input rule modules fuzzy inference model (functional-type SIRMs model, for short) which generalized consequent part of SIRMs model to function. However, it is too strict to satisfy the equivaence conditions of T–S inference model. Therefore, this paper proposes an extended functional-type SIRMs model (EF-SIRMs, for short) in which the consequent part of the functional-type SIRMs model is extended to a function with 1 dimensional polynomial from a function with n dimensional polynomial, and its properties are clarified. Further, it shows the ability of this model becomes greatly larger than that of ordinary functional-type SIRMs model. Moreover, it proposes a learning method of the EF-SIRMs model, and it is applied to a medical diagnosis, and compared with the conventional SIRMs models.

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