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Interpolativity of "at least-at most" models of monotone fuzzy rule bases: Multiple-input case
M. Štěpnička,
Published in World Scientific Publishing Co. Pte Ltd
2012
Volume: 7
   
Pages: 598 - 607
Abstract
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.
About the journal
JournalWorld Scientific Proc. Series on Computer Engineering and Information Science 7; Uncertainty Modeling in Knowledge Engineering and Decision Making - Proceedings of the 10th International FLINS Conf.
PublisherWorld Scientific Publishing Co. Pte Ltd