Reducing Computational Overhead by Improving the CRI and IRI Implication Step
In conventional SISO fuzzy expert systems (n-element input, m-element output), the implication step requires the O(n×m) operations using compositional rule-based inference (CRI) and individual rule-based inference (IRI). However, this introduces excessive complexity. This paper proposes two methods,...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2015-01-01
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| Series: | Journal of Control Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2015/725258 |
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| Summary: | In conventional SISO fuzzy expert systems (n-element input, m-element output), the implication step requires the O(n×m) operations using compositional rule-based inference (CRI) and individual rule-based inference (IRI). However, this introduces excessive complexity. This paper proposes two methods, sort compositional rule-based inference (SCRI) and sort individual rule-based inference (SIRI) aiming at reducing both temporal and spatial complexity by changing the operation of the implication step to O((n+m)log2(n+m)). We also propose a divide-and-conquer technique, called Quicksort, to verify the accuracy of SCRI and SIRI algorithms deployment to easily outperform the CRI and IRI methods. |
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| ISSN: | 1687-5249 1687-5257 |