An Evolutionary-Based Similarity Reasoning Scheme for Monotonic Multi-Input Fuzzy Inference Systems
In this paper, an Evolutionary-based Similarity Reasoning (ESR) scheme for preserving the monotonicity property of the multi-input Fuzzy Inference System (FIS) is proposed. Similarity reasoning (SR) is a useful solution for undertaking the incomplete rule base problem in FIS modeling. However, SR...
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| Main Authors: | , |
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| Format: | E-Article |
| Language: | English |
| Published: |
IEEE
2011
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/2733/1/An%20Evolutionary-Based%20Similarity%20Reasoning%20Scheme%20for%20Monotonic%20Multi-Input%20Fuzzy%20Inference%20Systems.pdf http://ir.unimas.my/id/eprint/2733/ |
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| Summary: | In this paper, an Evolutionary-based Similarity
Reasoning (ESR) scheme for preserving the monotonicity
property of the multi-input Fuzzy Inference System (FIS) is proposed. Similarity reasoning (SR) is a useful solution for undertaking the incomplete rule base problem in FIS modeling. However, SR may not be a direct solution to designing monotonic multi-input FIS models, owing to the difficulty in getting a set of monotonically-ordered conclusions. The proposed ESR scheme, which is a synthesis of evolutionary computing, sufficient
conditions, and SR, provides a useful solution to modeling and preserving the monotonicity property of multi-input FIS models. A case study on Failure Mode and Effect Analysis (FMEA) is used to demonstrate the effectiveness of the proposed ESR scheme in undertaking real world problems that require the monotonicity property of FIS models. |
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