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Building fuzzy inference systems with similarity reasoning : NSGAII-based fuzzy rule selection and evidential functions
conference contribution
posted on 2014-09-04, 00:00 authored by T L Jee, K C Chai, K M Tay, Chee Peng LimChee Peng LimIn our previous investigations, two Similarity Reasoning (SR)-based frameworks for tackling real-world problems have been proposed. In both frameworks, SR is used to deduce unknown fuzzy rules based on similarity of the given and unknown fuzzy rules for building a Fuzzy Inference System (FIS). In this paper, we further extend our previous findings by developing (1) a multi-objective evolutionary model for fuzzy rule selection; and (2) an evidential function to facilitate the use of both frameworks. The Non-Dominated Sorting Genetic Algorithms-p (NSGA-p) is adopted for fuzzy rule selection, in accordance with the Pareto optimal criterion. Besides that, two new evidential functions are developed, whereby given fuzzy rules are considered as evidence. Simulated and benchmark examples are included to demonstrate the applicability of these suggestions. Positive results were obtained.
History
Event
IEEE International Conference on Fuzzy Systems (2014 : Beijing, China)Pagination
2192 - 2197Publisher
IEEELocation
Beijing, ChinaPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2014-07-06End date
2014-07-11ISSN
1098-7584ISBN-13
9781479920723Language
engPublication classification
E Conference publication; E1 Full written paper - refereedCopyright notice
2014, Institute of Electrical and Electronics EngineersEditor/Contributor(s)
[Unknown]Title of proceedings
FUZZ-IEEE 2014 : Proceedings of the 2014 IEEE International Conference on Fuzzy SystemsUsage metrics
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