Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/34636
DC Field | Value | Language |
---|---|---|
dc.contributor | School of Nursing | en_US |
dc.creator | Deng, Z | en_US |
dc.creator | Choi, KS | en_US |
dc.creator | Cao, L | en_US |
dc.creator | Wang, S | en_US |
dc.date.accessioned | 2016-02-29T02:55:50Z | - |
dc.date.available | 2016-02-29T02:55:50Z | - |
dc.identifier.issn | 2162-237X | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/34636 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
dc.rights | The following publication Z. Deng, K. Choi, L. Cao and S. Wang, "T2FELA: Type-2 Fuzzy Extreme Learning Algorithm for Fast Training of Interval Type-2 TSK Fuzzy Logic System," in IEEE Transactions on Neural Networks and Learning Systems, vol. 25, no. 4, pp. 664-676, April 2014 is available at http://dx.doi.org/10.1109/TNNLS.2013.2280171. | en_US |
dc.title | T2FELA : type-2 fuzzy extreme learning algorithm for fast training of interval type-2 TSK fuzzy logic system | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 664 | en_US |
dc.identifier.epage | 676 | en_US |
dc.identifier.volume | 25 | en_US |
dc.identifier.issue | 4 | en_US |
dc.identifier.doi | 10.1109/TNNLS.2013.2280171 | en_US |
dcterms.abstract | A challenge in modeling type-2 fuzzy logic systems is the development of efficient learning algorithms to cope with the ever increasing size of real-world data sets. In this paper, the extreme learning strategy is introduced to develop a fast training algorithm for interval type-2 Takagi-Sugeno-Kang fuzzy logic systems. The proposed algorithm, called type-2 fuzzy extreme learning algorithm (T2FELA), has two distinctive characteristics. First, the parameters of the antecedents are randomly generated and parameters of the consequents are obtained by a fast learning method according to the extreme learning mechanism. In addition, because the obtained parameters are optimal in the sense of minimizing the norm, the resulting fuzzy systems exhibit better generalization performance. The experimental results clearly demonstrate that the training speed of the proposed T2FELA algorithm is superior to that of the existing state-of-the-art algorithms. The proposed algorithm also shows competitive performance in generalization abilities. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | IEEE transactions on neural networks and learning systems, Apr. 2014, v. 25, no. 4, p. 664-676 | en_US |
dcterms.isPartOf | IEEE transactions on neural networks and learning systems | en_US |
dcterms.issued | 2014-04 | - |
dc.identifier.pmid | 24807945 | - |
dc.identifier.eissn | 2162-2388 | en_US |
dc.identifier.rosgroupid | r70402 | - |
dc.description.ros | 2013-2014 > Academic research: refereed > Publication in refereed journal | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | a0597-n12 | - |
dc.identifier.SubFormID | 451 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.fundingText | PolyU5134/12E | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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a0597-n12_451.pdf | Pre-Published version | 1.55 MB | Adobe PDF | View/Open |
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