Parallel based support vector regression for empirical modeling of nonlinear chemical process systems
In this paper, a support vector regression (SVR) using radial basis function (RBF) kernel is proposed using an integrated parallel linear-and-nonlinear model framework for empirical modeling of nonlinear chemical process systems. Utilizing linear orthonormal basis filters (OBF) model to represent th...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Penerbit Universiti Kebangsaan Malaysia
2018
|
| Online Access: | http://journalarticle.ukm.my/12047/1/25%20Haslinda%20Zabiri.pdf |
| _version_ | 1796931605567111168 |
|---|---|
| author | Haslinda Zabiri, Ramasamy Marappagounder, Nasser M. Ramli, |
| author_facet | Haslinda Zabiri, Ramasamy Marappagounder, Nasser M. Ramli, |
| author_sort | Haslinda Zabiri, |
| collection | UKM |
| description | In this paper, a support vector regression (SVR) using radial basis function (RBF) kernel is proposed using an integrated parallel linear-and-nonlinear model framework for empirical modeling of nonlinear chemical process systems. Utilizing linear orthonormal basis filters (OBF) model to represent the linear structure, the developed empirical parallel model is tested for its performance under open-loop conditions in a nonlinear continuous stirred-tank reactor simulation case study as well as a highly nonlinear cascaded tank benchmark system. A comparative study between SVR and the parallel OBF-SVR models is then investigated. The results showed that the proposed parallel OBF-SVR model retained the same modelling efficiency as that of the SVR, whilst enhancing the generalization properties to out-of-sample data. |
| first_indexed | 2024-03-06T04:20:26Z |
| format | Article |
| id | ukm.eprints-12047 |
| institution | Universiti Kebangsaan Malaysia |
| language | English |
| last_indexed | 2024-03-06T04:20:26Z |
| publishDate | 2018 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| record_format | dspace |
| spelling | ukm.eprints-120472018-09-09T23:41:16Z http://journalarticle.ukm.my/12047/ Parallel based support vector regression for empirical modeling of nonlinear chemical process systems Haslinda Zabiri, Ramasamy Marappagounder, Nasser M. Ramli, In this paper, a support vector regression (SVR) using radial basis function (RBF) kernel is proposed using an integrated parallel linear-and-nonlinear model framework for empirical modeling of nonlinear chemical process systems. Utilizing linear orthonormal basis filters (OBF) model to represent the linear structure, the developed empirical parallel model is tested for its performance under open-loop conditions in a nonlinear continuous stirred-tank reactor simulation case study as well as a highly nonlinear cascaded tank benchmark system. A comparative study between SVR and the parallel OBF-SVR models is then investigated. The results showed that the proposed parallel OBF-SVR model retained the same modelling efficiency as that of the SVR, whilst enhancing the generalization properties to out-of-sample data. Penerbit Universiti Kebangsaan Malaysia 2018-03 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/12047/1/25%20Haslinda%20Zabiri.pdf Haslinda Zabiri, and Ramasamy Marappagounder, and Nasser M. Ramli, (2018) Parallel based support vector regression for empirical modeling of nonlinear chemical process systems. Sains Malaysiana, 47 (3). pp. 635-643. ISSN 0126-6039 http://www.ukm.my/jsm/malay_journals/jilid47bil3_2018/KandunganJilid47Bil3_2018.html |
| spellingShingle | Haslinda Zabiri, Ramasamy Marappagounder, Nasser M. Ramli, Parallel based support vector regression for empirical modeling of nonlinear chemical process systems |
| title | Parallel based support vector regression for empirical modeling of nonlinear chemical process systems |
| title_full | Parallel based support vector regression for empirical modeling of nonlinear chemical process systems |
| title_fullStr | Parallel based support vector regression for empirical modeling of nonlinear chemical process systems |
| title_full_unstemmed | Parallel based support vector regression for empirical modeling of nonlinear chemical process systems |
| title_short | Parallel based support vector regression for empirical modeling of nonlinear chemical process systems |
| title_sort | parallel based support vector regression for empirical modeling of nonlinear chemical process systems |
| url | http://journalarticle.ukm.my/12047/1/25%20Haslinda%20Zabiri.pdf |
| work_keys_str_mv | AT haslindazabiri parallelbasedsupportvectorregressionforempiricalmodelingofnonlinearchemicalprocesssystems AT ramasamymarappagounder parallelbasedsupportvectorregressionforempiricalmodelingofnonlinearchemicalprocesssystems AT nassermramli parallelbasedsupportvectorregressionforempiricalmodelingofnonlinearchemicalprocesssystems |