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On the use of fuzzy rule interpolation techniques for monotonic multi-input fuzzy rule base models

conference contribution
posted on 2009-01-01, 00:00 authored by K Tay, Chee Peng LimChee Peng Lim
Constructing a monotonicity relating function is important, as many engineering problems revolve around a monotonicity relationship between input(s) and output(s). In this paper, we investigate the use of fuzzy rule interpolation techniques for monotonicity relating fuzzy inference system (FIS). A mathematical derivation on the conditions of an FIS to be monotone is provided. From the derivation, two conditions are necessary. The derivation suggests that the mapped consequence fuzzy set of an FIS to be of a monotonicity order. We further evaluate the use of fuzzy rule interpolation techniques in predicting a consequent associated with an observation according to the monotonicity order. There are several findings in this article. We point out the importance of an ordering criterion in rule selection for a multi-input FIS before the interpolation process; and hence, the practice of choosing the nearest rules may not be true in this case. To fulfill the monotonicity order, we argue with an example that conventional fuzzy rule interpolation techniques that predict each consequence separately is not suitable in this case. We further suggest another class of interpolation techniques that predicts the consequence of a set of observations simultaneously, instead of separately. This can be accomplished with the use of a search algorithm, such as the brute force, genetic algorithm or etc.

History

Event

International Conference on Fuzzy Systems (2009 : Jeju Island, Korea)

Pagination

1736 - 1740

Publisher

IEEE

Location

Jeju Island, Korea

Place of publication

Piscataway, N. J.

Start date

2009-08-20

End date

2009-08-24

ISSN

1098-7584

ISBN-13

9781424435968

ISBN-10

142443596X

Language

eng

Publication classification

E1.1 Full written paper - refereed

Title of proceedings

FUZZ-IEEE 2009 : Proceedings of the IEEE International Conference on Fuzzy Systems