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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| Format: | Article |
| Language: | English |
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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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| _version_ | 1832548562505826304 |
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| author | Thoai Phu Vo Joy Iong-Zong Chen |
| author_facet | Thoai Phu Vo Joy Iong-Zong Chen |
| author_sort | Thoai Phu Vo |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ae156be4005c43f6be5b19204fe67f9d |
| institution | Kabale University |
| issn | 1687-5249 1687-5257 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Control Science and Engineering |
| spelling | doaj-art-ae156be4005c43f6be5b19204fe67f9d2025-02-03T06:13:44ZengWileyJournal of Control Science and Engineering1687-52491687-52572015-01-01201510.1155/2015/725258725258Reducing Computational Overhead by Improving the CRI and IRI Implication StepThoai Phu Vo0Joy Iong-Zong Chen1Department of Electrical Engineering, Dayeh University, No. 168, University Road, Changhua 51591, TaiwanDepartment of Electrical Engineering, Dayeh University, No. 168, University Road, Changhua 51591, TaiwanIn 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.http://dx.doi.org/10.1155/2015/725258 |
| spellingShingle | Thoai Phu Vo Joy Iong-Zong Chen Reducing Computational Overhead by Improving the CRI and IRI Implication Step Journal of Control Science and Engineering |
| title | Reducing Computational Overhead by Improving the CRI and IRI Implication Step |
| title_full | Reducing Computational Overhead by Improving the CRI and IRI Implication Step |
| title_fullStr | Reducing Computational Overhead by Improving the CRI and IRI Implication Step |
| title_full_unstemmed | Reducing Computational Overhead by Improving the CRI and IRI Implication Step |
| title_short | Reducing Computational Overhead by Improving the CRI and IRI Implication Step |
| title_sort | reducing computational overhead by improving the cri and iri implication step |
| url | http://dx.doi.org/10.1155/2015/725258 |
| work_keys_str_mv | AT thoaiphuvo reducingcomputationaloverheadbyimprovingthecriandiriimplicationstep AT joyiongzongchen reducingcomputationaloverheadbyimprovingthecriandiriimplicationstep |