DocumentCode :
227100
Title :
Medical diagnosis and monotonicity clarification using SIRMs connected fuzzy inference model with functional weights
Author :
Seki, Hiroshi ; Nakashima, Takayoshi
Author_Institution :
Kwansei Gakuin Univ., Sanda, Japan
fYear :
2014
fDate :
6-11 July 2014
Firstpage :
1662
Lastpage :
1665
Abstract :
This paper discusses the SIRMs (Single-Input Rule Modules) connected fuzzy inference model with functional weights (SIRMs model with FW). The SIRMs model with FW consists of a number of groups of simple fuzzy if-then rules with only a single attribute in the antecedent part. The final outputs of conventional SIRMs model are obtained by summarizing product of the functional weight and inference result from a rule module. In the SIRMs model of the paper, we firstly clarify its monotonicity. Secondly, we apply the SIRMs model with FW to medical diagnosis.
Keywords :
fuzzy reasoning; fuzzy set theory; medical diagnostic computing; SIRM connected fuzzy inference model; functional weights; fuzzy if-then rules; medical diagnosis; monotonicity clarification; single-input rule modules; Computational modeling; Data models; Diabetes; Fuzzy logic; Inference algorithms; Medical diagnosis; Medical diagnostic imaging; Fuzzy inference; Single Input Rule Modules (SIRMs) connected fuzzy inference model; functional weight; medical data; monotonicity;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Systems (FUZZ-IEEE), 2014 IEEE International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4799-2073-0
Type :
conf
DOI :
10.1109/FUZZ-IEEE.2014.6891866
Filename :
6891866
Link To Document :
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