Interpolativity of "at least-at most" models of monotone fuzzy rule bases: Multiple-input case

Stepnicka, M and Jayaram, Balasubramaniam (2012) Interpolativity of "at least-at most" models of monotone fuzzy rule bases: Multiple-input case. In: 10th International Fuzzy Logic and Intelligent Technologies inNuclear Science Conference, 26-29, August 2012, Istanbul; Turkey.

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Among the many desirable properties of fuzzy inference systems not all of them are known to co-exist. For instance, a system based on a monotone fuzzy rule base need not be monotonic and interpolative simultaneously. Recently, Štěpnička and De Baets have investigated and shown the co-existence of the above two properties in the case of a fuzzy relational inference systems and the single-input-single-output (SISO) rule bases. An extension of these results to the multiple-input-single-output (MISO) case is not straight-forward owing to the lack of a natural ordering in higher dimensions. In this work, we study the MISO case and show that similar results are available when the monotone rule base is modeled based on at-most and at-least modifiers.

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IITH Creators:
IITH CreatorsORCiD
Jayaram, Balasubramaniam
Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Fuzzy inference systems; Fuzzy rule base; Fuzzy rule basis; Higher dimensions; Interpolativity; Multiple inputs; Relational inferences; Single-input single-output
Subjects: Mathematics
Divisions: Department of Mathematics
Depositing User: Team Library
Date Deposited: 30 Oct 2014 07:22
Last Modified: 20 Sep 2017 08:54
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