Refinement of Fuzzy Production Rules by Using a Fuzzy-Neural Approach
In this paper we develop a fuzzy neural network (FNN) with a new BP learning algorithm using some smooth function, which is used to tune the local and global weights of fuzzy production rules (FPRs) so as to enhance the representation power of FPRs. The aim of including local and global weights in F...
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| creator | Huang, Dong-mei Ha, Ming-hu Li, Ya-min Tsang, Eric C. C. |
| description | In this paper we develop a fuzzy neural network (FNN) with a new BP learning algorithm using some smooth function, which is used to tune the local and global weights of fuzzy production rules (FPRs) so as to enhance the representation power of FPRs. The aim of including local and global weights in FPRs and the tuning of these weights is to improve the learning and testing accuracy without increasing the number of rules. By experimenting with some existing benchmark examples (Iris data, Wine data, Pima data and Glass data) the proposed method is found to have high accuracy in classifying unseen samples without increasing the number of the extracted FPRs, and furthermore, the time required to consult with domain experts for gaining a rule is reduced. The synergy between WFPRs and an FNN offers a new insight into the construction of better fuzzy intelligent systems in the future. |
| doi_str_mv | 10.1007/11739685_54 |
| format | Conference Proceeding |
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C.</creator><contributor>Yan, Hong ; Yeung, Daniel S. ; Wang, Xi-Zhao ; Liu, Zhi-Qiang</contributor><creatorcontrib>Huang, Dong-mei ; Ha, Ming-hu ; Li, Ya-min ; Tsang, Eric C. C. ; Yan, Hong ; Yeung, Daniel S. ; Wang, Xi-Zhao ; Liu, Zhi-Qiang</creatorcontrib><description>In this paper we develop a fuzzy neural network (FNN) with a new BP learning algorithm using some smooth function, which is used to tune the local and global weights of fuzzy production rules (FPRs) so as to enhance the representation power of FPRs. The aim of including local and global weights in FPRs and the tuning of these weights is to improve the learning and testing accuracy without increasing the number of rules. By experimenting with some existing benchmark examples (Iris data, Wine data, Pima data and Glass data) the proposed method is found to have high accuracy in classifying unseen samples without increasing the number of the extracted FPRs, and furthermore, the time required to consult with domain experts for gaining a rule is reduced. 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C.</creatorcontrib><title>Refinement of Fuzzy Production Rules by Using a Fuzzy-Neural Approach</title><title>Advances in Machine Learning and Cybernetics</title><description>In this paper we develop a fuzzy neural network (FNN) with a new BP learning algorithm using some smooth function, which is used to tune the local and global weights of fuzzy production rules (FPRs) so as to enhance the representation power of FPRs. The aim of including local and global weights in FPRs and the tuning of these weights is to improve the learning and testing accuracy without increasing the number of rules. By experimenting with some existing benchmark examples (Iris data, Wine data, Pima data and Glass data) the proposed method is found to have high accuracy in classifying unseen samples without increasing the number of the extracted FPRs, and furthermore, the time required to consult with domain experts for gaining a rule is reduced. The synergy between WFPRs and an FNN offers a new insight into the construction of better fuzzy intelligent systems in the future.</description><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Computer science; control theory; systems</subject><subject>Exact sciences and technology</subject><subject>Pattern recognition. Digital image processing. 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Computational geometry</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Huang, Dong-mei</creatorcontrib><creatorcontrib>Ha, Ming-hu</creatorcontrib><creatorcontrib>Li, Ya-min</creatorcontrib><creatorcontrib>Tsang, Eric C. C.</creatorcontrib><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Huang, Dong-mei</au><au>Ha, Ming-hu</au><au>Li, Ya-min</au><au>Tsang, Eric C. 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By experimenting with some existing benchmark examples (Iris data, Wine data, Pima data and Glass data) the proposed method is found to have high accuracy in classifying unseen samples without increasing the number of the extracted FPRs, and furthermore, the time required to consult with domain experts for gaining a rule is reduced. The synergy between WFPRs and an FNN offers a new insight into the construction of better fuzzy intelligent systems in the future.</abstract><cop>Berlin, Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/11739685_54</doi><tpages>11</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0302-9743 |
| ispartof | Advances in Machine Learning and Cybernetics, 2006, p.517-527 |
| issn | 0302-9743 1611-3349 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_19686490 |
| source | Springer Books |
| subjects | Applied sciences Artificial intelligence Computer science control theory systems Exact sciences and technology Pattern recognition. Digital image processing. Computational geometry |
| title | Refinement of Fuzzy Production Rules by Using a Fuzzy-Neural Approach |
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