Weighted fuzzy production rule extraction using modified harmony search algorithm and BP neural network framework
Compared with rules in the form of 'IF-THEN,' weighted fuzzy production rules (WFPRs) have more robust knowledge expression capabilities, but weighted fuzzy production rules are more difficult to obtain. The weighted fuzzy production rules obtained using traditional neural network methods...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Institute of Electrical and Electronics Engineers Inc.
2020
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| Subjects: | |
| Online Access: | http://eprints.utm.my/91406/1/AzlanMohdZain2020_WeightedFuzzyProductionRuleExtraction.pdf |
| _version_ | 1796865221110792192 |
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| author | Li, Hang Cheng Zhou, Kai Qing Mo, Li Ping Mohd. Zain, Azlan Qin, Feng |
| author_facet | Li, Hang Cheng Zhou, Kai Qing Mo, Li Ping Mohd. Zain, Azlan Qin, Feng |
| author_sort | Li, Hang Cheng |
| collection | ePrints |
| description | Compared with rules in the form of 'IF-THEN,' weighted fuzzy production rules (WFPRs) have more robust knowledge expression capabilities, but weighted fuzzy production rules are more difficult to obtain. The weighted fuzzy production rules obtained using traditional neural network methods have shortcomings, such as insufficient precision and insufficient knowledge extraction. Focusing on the mentioned shortages, a modified weighted fuzzy production rules extraction approach is proposed by combining the modified harmony search algorithm, and neural network. The method consists of three main stages. First, a global optimal adaptive harmony search algorithm (AGOHS) is proposed to overcome the traditional harmony search algorithm's existing poor adaptive ability. Then, the AGOHS algorithm is used to optimize the neural network's initial weights to improve the neural network's training efficiency. Finally, extract the WFPRs with IF-THEN from the trained neural network and give the corresponding fuzzy reasoning. Through the WFPRs extraction experiments using IRIS and PIMA data sets reveal the proposed rule extraction framework has some apparent highlights, such as high accuracy, the smaller number of generated rules, and low redundancy. |
| first_indexed | 2024-03-05T20:53:39Z |
| format | Article |
| id | utm.eprints-91406 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T20:53:39Z |
| publishDate | 2020 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| record_format | dspace |
| spelling | utm.eprints-914062021-06-30T12:16:07Z http://eprints.utm.my/91406/ Weighted fuzzy production rule extraction using modified harmony search algorithm and BP neural network framework Li, Hang Cheng Zhou, Kai Qing Mo, Li Ping Mohd. Zain, Azlan Qin, Feng QA Mathematics Compared with rules in the form of 'IF-THEN,' weighted fuzzy production rules (WFPRs) have more robust knowledge expression capabilities, but weighted fuzzy production rules are more difficult to obtain. The weighted fuzzy production rules obtained using traditional neural network methods have shortcomings, such as insufficient precision and insufficient knowledge extraction. Focusing on the mentioned shortages, a modified weighted fuzzy production rules extraction approach is proposed by combining the modified harmony search algorithm, and neural network. The method consists of three main stages. First, a global optimal adaptive harmony search algorithm (AGOHS) is proposed to overcome the traditional harmony search algorithm's existing poor adaptive ability. Then, the AGOHS algorithm is used to optimize the neural network's initial weights to improve the neural network's training efficiency. Finally, extract the WFPRs with IF-THEN from the trained neural network and give the corresponding fuzzy reasoning. Through the WFPRs extraction experiments using IRIS and PIMA data sets reveal the proposed rule extraction framework has some apparent highlights, such as high accuracy, the smaller number of generated rules, and low redundancy. Institute of Electrical and Electronics Engineers Inc. 2020-10 Article PeerReviewed application/pdf en http://eprints.utm.my/91406/1/AzlanMohdZain2020_WeightedFuzzyProductionRuleExtraction.pdf Li, Hang Cheng and Zhou, Kai Qing and Mo, Li Ping and Mohd. Zain, Azlan and Qin, Feng (2020) Weighted fuzzy production rule extraction using modified harmony search algorithm and BP neural network framework. IEEE Access, 8 . pp. 186620-186637. ISSN 2169-3536 http://dx.doi.org/10.1109/ACCESS.2020.3029966 DOI:10.1109/ACCESS.2020.3029966 |
| spellingShingle | QA Mathematics Li, Hang Cheng Zhou, Kai Qing Mo, Li Ping Mohd. Zain, Azlan Qin, Feng Weighted fuzzy production rule extraction using modified harmony search algorithm and BP neural network framework |
| title | Weighted fuzzy production rule extraction using modified harmony search algorithm and BP neural network framework |
| title_full | Weighted fuzzy production rule extraction using modified harmony search algorithm and BP neural network framework |
| title_fullStr | Weighted fuzzy production rule extraction using modified harmony search algorithm and BP neural network framework |
| title_full_unstemmed | Weighted fuzzy production rule extraction using modified harmony search algorithm and BP neural network framework |
| title_short | Weighted fuzzy production rule extraction using modified harmony search algorithm and BP neural network framework |
| title_sort | weighted fuzzy production rule extraction using modified harmony search algorithm and bp neural network framework |
| topic | QA Mathematics |
| url | http://eprints.utm.my/91406/1/AzlanMohdZain2020_WeightedFuzzyProductionRuleExtraction.pdf |
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