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Building fuzzy inference systems with similarity reasoning : NSGAII-based fuzzy rule selection and evidential functions

Version 2 2024-06-06, 08:05
Version 1 2015-03-11, 15:11
conference contribution
posted on 2024-06-06, 08:05 authored by TL Jee, KC Chai, KM Tay, Chee Peng Lim
In 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

Pagination

2192-2197

Location

Beijing, China

Start date

2014-07-06

End date

2014-07-11

ISSN

1098-7584

ISBN-13

9781479920723

Language

eng

Publication classification

E Conference publication, E1 Full written paper - refereed

Copyright notice

2014, Institute of Electrical and Electronics Engineers

Editor/Contributor(s)

[Unknown]

Title of proceedings

FUZZ-IEEE 2014 : Proceedings of the 2014 IEEE International Conference on Fuzzy Systems

Event

IEEE International Conference on Fuzzy Systems (2014 : Beijing, China)

Publisher

IEEE

Place of publication

Piscataway, N.J.