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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kayhan, Gokhan | - |
dc.contributor.author | Ozdemir, Ali Ekber | - |
dc.contributor.author | Eminoglu, Ilyas | - |
dc.date.accessioned | 2022-09-07T06:59:42Z | - |
dc.date.available | 2022-09-07T06:59:42Z | - |
dc.date.issued | 2013 | - |
dc.identifier.uri | http://doi.org/10.1007/s00521-012-1053-8 | - |
dc.identifier.uri | http://earsiv.odu.edu.tr:8080/xmlui/handle/11489/3204 | - |
dc.description.abstract | This paper reviews some frequently used methods to initialize an radial basis function (RBF) network and presents systematic design procedures for pre-processing unit(s) to initialize RBF network from available input-output data sets. The pre-processing units are computationally hybrid two-step training algorithms that can be named as (1) construction of initial structure and (2) coarse-tuning of free parameters. The first step, the number, and the locations of the initial centers of RBF network can be determined. Thus, an orthogonal least squares algorithm and a modified counter propagation network can be employed for this purpose. In the second step, a coarse-tuning of free parameters is achieved by using clustering procedures. Thus, the Gustafson-Kessel and the fuzzy C-means clustering methods are evaluated for the coarse-tuning. The first two-step behaves like a pre-processing unit for the last stage (or fine-tuning stage-a gradient descent algorithm). The initialization ability of the proposed four pre-processing units (modular combination of the existing methods) is compared with three non-linear benchmarks in terms of root mean square errors. Finally, the proposed hybrid pre-processing units may initialize a fairly accurate, IF-THEN-wise readable initial model automatically and efficiently with a minimum user inference. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | SPRINGER LONDON LTD236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND | en_US |
dc.relation.isversionof | 10.1007/s00521-012-1053-8 | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | Counter propagation network (CPN) Fuzzy C-means (FCM) Gustafson-Kessel (GK) | en_US |
dc.title | Reviewing and designing pre-processing units for RBF networks: initial structure identification and coarse-tuning of free parameters | en_US |
dc.type | article | en_US |
dc.relation.journal | NEURAL COMPUTING & APPLICATIONS | en_US |
dc.contributor.department | Ordu Üniversitesi | en_US |
dc.contributor.authorID | 0000-0002-3367-8390 | en_US |
dc.identifier.volume | 22 | en_US |
dc.identifier.issue | 7-8 | en_US |
dc.identifier.startpage | 1655 | en_US |
dc.identifier.endpage | 1666 | en_US |
Appears in Collections: | Deniz Bilimleri ve Teknolojisi |
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