Nonlinear Identification Using Single Input Connected Fuzzy Inference Model

The single input connected fuzzy inference model (SIC model) by Hayashi et al. can decrease the number of fuzzy rules drastically in comparison with the conventional fuzzy inference models. In this paper, we first show the SIC model and its learning algorithm, and clarify the applicability of the SI...

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Bibliographic Details
Published in:Procedia computer science 2013, Vol.22, p.1121-1125
Main Author: Seki, Hirosato
Format: Article
Language:English
Subjects:
Approximate reasoning
Fuzzy inference systems
Nonlinear identification
Single Input Connected (SIC) fuzzy inference model
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Summary:The single input connected fuzzy inference model (SIC model) by Hayashi et al. can decrease the number of fuzzy rules drastically in comparison with the conventional fuzzy inference models. In this paper, we first show the SIC model and its learning algorithm, and clarify the applicability of the SIC model by applying it to identification of nonlinear functions.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2013.09.198