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 |