Radial basis function (RBF) for non-linear dynamic system identification
One of the key problem in system identification is finding a suitable model structure. In this paper, radial basis function (RBF) network using various basis functions are trained to represent discrete-time nonlinear dynamic systems and the results are compared. The orthogonal least squarealgorithm...
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| Format: | Article |
| Language: | en |
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Penerbit UTM Press
2002
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| Online Access: | http://eprints.utm.my/1301/1/JT36A4.pdf http://eprints.utm.my/1301/ http://www.penerbit.utm.my/onlinejournal/36/A/JT36A4.pdf |
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| _version_ | 1845470439877378048 |
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| author | Ahmad, Robiah Jamaluddin, Hishamuddin |
| author_facet | Ahmad, Robiah Jamaluddin, Hishamuddin |
| author_sort | Ahmad, Robiah |
| building | UTM Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknologi Malaysia |
| content_source | UTM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | One of the key problem in system identification is finding a suitable model structure. In this paper, radial basis function (RBF) network using various basis functions are trained to represent discrete-time nonlinear dynamic systems and the results are compared. The orthogonal least squarealgorithm is employed to select parsimonious RBF models. To demonstrate the identification procedure two examples of modelling on linear system were included. |
| format | Article |
| id | my.utm.eprints-1301 |
| institution | Universiti Teknologi Malaysia |
| language | en |
| publishDate | 2002 |
| publisher | Penerbit UTM Press |
| record_format | eprints |
| spelling | my.utm.eprints-13012017-11-01T04:17:44Z http://eprints.utm.my/1301/ Radial basis function (RBF) for non-linear dynamic system identification Ahmad, Robiah Jamaluddin, Hishamuddin TJ Mechanical engineering and machinery One of the key problem in system identification is finding a suitable model structure. In this paper, radial basis function (RBF) network using various basis functions are trained to represent discrete-time nonlinear dynamic systems and the results are compared. The orthogonal least squarealgorithm is employed to select parsimonious RBF models. To demonstrate the identification procedure two examples of modelling on linear system were included. Penerbit UTM Press 2002-06 Article PeerReviewed application/pdf en http://eprints.utm.my/1301/1/JT36A4.pdf Ahmad, Robiah and Jamaluddin, Hishamuddin (2002) Radial basis function (RBF) for non-linear dynamic system identification. Jurnal Teknologi A (36A). pp. 39-54. ISSN 0127-9696 http://www.penerbit.utm.my/onlinejournal/36/A/JT36A4.pdf |
| spellingShingle | TJ Mechanical engineering and machinery Ahmad, Robiah Jamaluddin, Hishamuddin Radial basis function (RBF) for non-linear dynamic system identification |
| title | Radial basis function (RBF) for non-linear dynamic system identification |
| title_full | Radial basis function (RBF) for non-linear dynamic system identification |
| title_fullStr | Radial basis function (RBF) for non-linear dynamic system identification |
| title_full_unstemmed | Radial basis function (RBF) for non-linear dynamic system identification |
| title_short | Radial basis function (RBF) for non-linear dynamic system identification |
| title_sort | radial basis function (rbf) for non-linear dynamic system identification |
| topic | TJ Mechanical engineering and machinery |
| url | http://eprints.utm.my/1301/1/JT36A4.pdf http://eprints.utm.my/1301/ http://www.penerbit.utm.my/onlinejournal/36/A/JT36A4.pdf |
| url_provider | http://eprints.utm.my/ |