| Flexible neuro-fuzzy systems. | |
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MedLine Citation:
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PMID: 18238039 Owner: NLM Status: In-Data-Review |
Abstract/OtherAbstract:
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In this paper, we derive new neuro-fuzzy structures called flexible neuro-fuzzy inference systems or FLEXNFIS. Based on the input-output data, we learn not only the parameters of the membership functions but also the type of the systems (Mamdani or logical). Moreover, we introduce: 1) softness to fuzzy implication operators, to aggregation of rules and to connectives of antecedents; 2) certainty weights to aggregation of rules and to connectives of antecedents; and 3) parameterized families of T-norms and S-norms to fuzzy implication operators, to aggregation of rules and to connectives of antecedents. Our approach introduces more flexibility to the structure and design of neuro-fuzzy systems. Through computer simulations, we show that Mamdani-type systems are more suitable to approximation problems, whereas logical-type systems may be preferred for classification problems. |
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Authors:
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L Rutkowski; K Cpalka |
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Publication Detail:
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Type: Journal Article |
Journal Detail:
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Title: IEEE transactions on neural networks / a publication of the IEEE Neural Networks Council Volume: 14 ISSN: 1045-9227 ISO Abbreviation: IEEE Trans Neural Netw Publication Date: 2003 |
Date Detail:
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Created Date: 2008-02-01 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 101211035 Medline TA: IEEE Trans Neural Netw Country: United States |
Other Details:
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Languages: eng Pagination: 554-74 Citation Subset: - |
Affiliation:
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Dept. of Comput. Eng., Tech. Univ. of Czestochowa, Poland. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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