On the Learning Method, Properties of the Extended Functional-Type SIRMs Connected Fuzzy Inference Model and Their Application to a Medical Diagnosis System
Diederik van Krieken*, Hirosato Seki**, and Masahiro Inuiguchi**
*University of Groningen
43-2 John Franklinstraat, Amsterdam 1056 SX, the Netherlands
**Graduate School of Engineering Science, Osaka University
1-3 Machikaneyama-cho, Toyonaka, Osaka 560-8531, Japan
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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