Building Monotonicity-Preserving Fuzzy Inference Models with Optimization-Based Similarity Reasoning and a Monotonicity Index
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...
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| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2012
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/2736/1/Building%20Monotonicity-Preserving%20Fuzzy%20Inference%20Models%20with%20Optimization-Based%20Similarity%20Reasoning%20and%20a%20Monotonicity%20Index.pdf http://ir.unimas.my/id/eprint/2736/ |
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| Institution: | Universiti Malaysia Sarawak |
| Language: | English |
| Summary: | 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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