Complex-valued SIRMs connected fuzzy inference model

Author

Seki, Hiroshi ; Nakashima, Takayoshi

Author_Institution

Dept. of Math. Sci., Kwansei Gakuin Univ., Nishinomiya, Japan

fYear

2014

fDate

22-24 Oct. 2014

Firstpage

250

Lastpage

253

Abstract

The single input rule modules connected type fuzzy inference model (SIRMs model) that unifies the inference outputs from fuzzy rule modules of one input type "IF-THEN" form can sharply reduce the number of fuzzy rules. However, since the number of rules of SIRMs model was limited as compared to the traditional fuzzy inference model, inference results gained by the SIRMs model were simple in general. Therefore, this paper proposes a complex-valued SIRMs connected fuzzy inference model (CV-SIRM model) in which the antecedent parts are extended to complex-valued fuzzy sets from conventional fuzzy sets. Moreover, it shows the properties and applicability of the proposed model.

Keywords

fuzzy reasoning; fuzzy set theory; CV-SIRM model; complex-valued SIRM; fuzzy inference model; fuzzy rule modules; fuzzy sets; if-then form; single input rule modules; Computational modeling; Fuzzy logic; Fuzzy sets; Heuristic algorithms; Inference algorithms; Learning systems; Neural networks; Approximate reasoning; Complex-valued fuzzy sets; Fuzzy inference systems; Single Input Rule Modules (SIRMs) connected fuzzy inference model;

fLanguage

English

Publisher

ieee

Conference_Titel

Granular Computing (GrC), 2014 IEEE International Conference on

Conference_Location

Noboribetsu

Type

conf

DOI

10.1109/GRC.2014.6982844

Filename

6982844