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Optimization of gaussian fuzzy membership functions and evaluation of the monotonicity property of fuzzy inference systems

conference contribution
posted on 2011-01-01, 00:00 authored by K Tay, Chee Peng LimChee Peng Lim
In this paper, two issues relating to modeling of a monotonicity-preserving Fuzzy Inference System (FIS) are examined. The first is on designing or tuning of Gaussian Membership Functions (MFs) for a monotonic FIS. Designing Gaussian MFs for an FIS is difficult because of its spreading and curvature characteristics. In this study, the sufficient conditions are exploited, and the procedure of designing Gaussian MFs is formulated as a constrained optimization problem. The second issue is on the testing procedure for a monotonic FIS. As such, a testing procedure for a monotonic FIS model is proposed. Applicability of the proposed approach is demonstrated with a real world industrial application, i.e., Failure Mode and Effect Analysis. The results obtained are analysis and discussed. The outcomes show that the proposed approach is useful in designing a monotonicity-preserving FIS model.

History

Event

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

Pagination

1219 - 1224

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