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A monotonicity index for the monotone fuzzy modeling problem
conference contribution
posted on 2012-01-01, 00:00 authored by K Tay, Chee Peng LimChee Peng Lim, T JeeIn this paper, the problem of maintaining the (global) monotonicity and local monotonicity properties between the input(s) and the output of an FIS model is addressed. This is known as the monotone fuzzy modeling problem. In our previous work, this problem has been tackled by developing some mathematical conditions for an FIS model to observe the monotonicity property. These mathematical conditions are used as a set of governing equations for undertaking FIS modeling problems, and have been extended to some advanced FIS modeling techniques. Here, we examine an alternative to the monotone fuzzy modeling problem by introducing a monotonicity index. The monotonicity index is employed as an approximate indicator to measure the fulfillment of an FIS model to the monotonicity property. It allows the FIS model to be constructed using an optimization method, or be tuned to achieve a better performance, without knowing the exact mathematical conditions of the FIS model to satisfy the monotonicity property. Besides, the monotonicity index can be extended to FIS modeling that involves the local monotonicity problem. We also analyze the relationship between the FIS model and its monotonicity property fulfillment, as well as derived mathematical conditions, using the Monte Carlo method.
History
Event
International Conference on Fuzzy Systems (2012 : Brisbane, Qld.)Pagination
1 - 8Publisher
IEEELocation
Brisbane, Qld.Place of publication
[Piscataway, N. J.]Publisher DOI
Start date
2012-06-10End date
2012-06-15ISSN
1098-7584ISBN-13
9781467315074ISBN-10
1467315079Language
engPublication classification
E1.1 Full written paper - refereedTitle of proceedings
FUZZ-IEEE 2012 : Proceedings of the IEEE 2012 International Conference on Fuzzy SystemsUsage metrics
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fuzzy inference systemmonotonicity propertymonotonicity indexthe sufficient conditionsmonto carloevolutionary computation optimizationsystem identificationScience & TechnologyTechnologyComputer Science, Artificial IntelligenceEngineering, Electrical & ElectronicComputer ScienceEngineeringINFERENCE TECHNIQUESSYSTEMS
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