A New Monotonicity Index for Fuzzy Rule-based Systems
A search in the literature reveals that mathematical conditions (usually sufficient conditions) for the Fuzzy Inference System (FIS) models to satisfy the monotonicity property have been developed. A monotonicallyordered fuzzy rule base is important to maintain the monotonicity property of an FIS....
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| Main Authors: | , , |
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| Format: | E-Article |
| Language: | English |
| Published: |
IEEE
2014
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/5182/1/a%20new%20monotonicity%20index%20for%20fuzzy%20rule-based%20systems%20%28abstract%29.pdf http://ir.unimas.my/id/eprint/5182/ |
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| Institution: | Universiti Malaysia Sarawak |
| Language: | English |
| Summary: | A search in the literature reveals that mathematical conditions (usually sufficient conditions) for the
Fuzzy Inference System (FIS) models to satisfy the
monotonicity property have been developed. A monotonicallyordered fuzzy rule base is important to maintain the monotonicity property of an FIS. However, it may difficult to obtain a monotonically-ordered fuzzy rule base in practice. We have previously introduced the idea of fuzzy rule relabeling to tackle this problem. In this paper, we further propose a monotonicity index for the FIS system, which serves as a metric to indicate the degree of a fuzzy rule base fulfilling the monotonicity property. The index is useful to provide an indication whether a fuzzy rule base should (or should not) be
used in practice, even with fuzzy rule relabeling. To illustrate the idea, the zero-order Sugeno FIS model is exemplified. We add noise as errors into the fuzzy rule base to formulate a set of non-monotone fuzzy rules. As such, the metric also acts as a measure of noise in the fuzzy rule base. The results show that the proposed metric is useful to indicate the degree of a fuzzy
rule base fulfilling the monotonicity property. |
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