Building Monotonicity-Preserving Fuzzy Inference Models with Optimization-Based Similarity Reasoning and a Monotonicity Index

Kai, M.T and Chee, P.L and Tze, L.J (2012) Building Monotonicity-Preserving Fuzzy Inference Models with Optimization-Based Similarity Reasoning and a Monotonicity Index. In: WCCI 2012 IEEE World Congress on Computational Intelligence, June, 10-15, 2012.

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Abstract

In this paper, a novel approach to building a Fuzzy Inference System (FIS) that preserves the monotonicity property is proposed. A new fuzzy re-labeling technique to re-label the consequents of fuzzy rules in the database (before the Similarity Reasoning process) and a monotonicity index for use in FIS modeling are introduced. The proposed approach is able to overcome several restrictions in our previous work that uses mathematical conditions in building monotonicity-preserving FIS models. Here, we show that the proposed approach is applicable to different FIS models, which include the zero-order Sugeno FIS and Mamdani models. Besides, the proposed approach can be extended to undertake problems related to the local monotonicity property of FIS models. A number of examples to demonstrate the usefulness of the proposed approach are presented. The results indicate the usefulness of the proposed approach in constructing monotonicity-preserving FIS models.

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