You are not logged in.

A new fuzzy ratio and its application to the single input rule modules connected fuzzy inference system

Jong,CH, Tay,KM and Lim,CP 2014, A new fuzzy ratio and its application to the single input rule modules connected fuzzy inference system, in FUZZ-IEEE 2014 : Proceedings of the 2014 IEEE International Conference on Fuzzy Systems, IEEE, Piscataway, N.J., pp. 1586-1590, doi: 10.1109/FUZZ-IEEE.2014.6891614.

Attached Files
Name Description MIMEType Size Downloads

Title A new fuzzy ratio and its application to the single input rule modules connected fuzzy inference system
Author(s) Jong,CH
Tay,KM
Lim,CPORCID iD for Lim,CP orcid.org/0000-0003-4191-9083
Conference name IEEE International Conference on Fuzzy Systems (2014 : Beijing, China)
Conference location Beijing, China
Conference dates 6-11 Jul. 2014
Title of proceedings FUZZ-IEEE 2014 : Proceedings of the 2014 IEEE International Conference on Fuzzy Systems
Editor(s) [Unknown]
Publication date 2014
Conference series IEEE International Conference on Fuzzy Systems
Start page 1586
End page 1590
Total pages 5
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) fuzzy arithmetic
Fuzzy ratio
fuzzy weights
single input rule modules connected fuzzy inference system
Summary The principle of ratios has been applied to many real world problems, e.g. the part-to-part and part-to-whole ratio formulations. As it is difficult for humans to provide an exact ratio in many real situations, we introduce a fuzzy ratio in this paper. We use some notions from fuzzy arithmetic to analyze fuzzy ratios captured from humans. An application of the formulated fuzzy ratio to a Single Input Rule Modules connected Fuzzy Inference System (SIRMs-FIS) is demonstrated. Instead of using a precise weight, fuzzy sets are employed to represent the relative importance of each rule module. The resulting fuzzy weights are explained as a fuzzy ratio on a weight domain. In addition, a new SIRMs-FIS model with fuzzy weights and part-to-whole fuzzy ratio is devised. A simulated example is presented to clarify the proposed SIRM-FIS model.
ISBN 9781479920723
ISSN 1098-7584
Language eng
DOI 10.1109/FUZZ-IEEE.2014.6891614
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2014, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30070581

Document type: Conference Paper
Collection: Centre for Intelligent Systems Research
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 0 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 118 Abstract Views, 3 File Downloads  -  Detailed Statistics
Created: Tue, 24 Mar 2015, 15:21:29 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.