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An evolutionary-based similarity reasoning scheme for monotonic multi-input fuzzy inference systems

conference contribution
posted on 2011-01-01, 00:00 authored by K Tay, Chee Peng LimChee Peng Lim
In this paper, an Evolutionary-based Similarity Reasoning (ESR) scheme for preserving the monotonicity property of the multi-input Fuzzy Inference System (FIS) is proposed. Similarity reasoning (SR) is a useful solution for undertaking the incomplete rule base problem in FIS modeling. However, SR may not be a direct solution to designing monotonic multi-input FIS models, owing to the difficulty in getting a set of monotonically-ordered conclusions. The proposed ESR scheme, which is a synthesis of evolutionary computing, sufficient conditions, and SR, provides a useful solution to modeling and preserving the monotonicity property of multi-input FIS models. A case study on Failure Mode and Effect Analysis (FMEA) is used to demonstrate the effectiveness of the proposed ESR scheme in undertaking real world problems that require the monotonicity property of FIS models.

History

Event

International Conference on Fuzzy Systems (2011 : Taipei, Taiwan)

Pagination

442 - 447

Publisher

IEEE Computer Society

Location

Taipei, Taiwan

Place of publication

Los Alamitos, Calif.

Start date

2011-06-27

End date

2011-06-30

ISSN

1098-7584

ISBN-13

9781424473151

ISBN-10

1424473152

Language

eng

Publication classification

E1.1 Full written paper - refereed

Copyright notice

2011, IEEE

Title of proceedings

FUZZ 2011 : Proceedings of the IEEE International Conference on Fuzzy Systems