Nonlinear System Identification Using Quasi-ARX RBFN Models with a Parameter-Classified Scheme
Quasi-linear autoregressive with exogenous inputs (Quasi-ARX) models have received considerable attention for their usefulness in nonlinear system identification and control. In this paper, identification methods of quasi-ARX type models are reviewed and categorized in three main groups, and a two-s...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2017/8197602 |
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| author | Lan Wang Yu Cheng Jinglu Hu Jinling Liang Abdullah M. Dobaie |
| author_facet | Lan Wang Yu Cheng Jinglu Hu Jinling Liang Abdullah M. Dobaie |
| author_sort | Lan Wang |
| collection | DOAJ |
| description | Quasi-linear autoregressive with exogenous inputs (Quasi-ARX) models have received considerable attention for their usefulness in nonlinear system identification and control. In this paper, identification methods of quasi-ARX type models are reviewed and categorized in three main groups, and a two-step learning approach is proposed as an extension of the parameter-classified methods to identify the quasi-ARX radial basis function network (RBFN) model. Firstly, a clustering method is utilized to provide statistical properties of the dataset for determining the parameters nonlinear to the model, which are interpreted meaningfully in the sense of interpolation parameters of a local linear model. Secondly, support vector regression is used to estimate the parameters linear to the model; meanwhile, an explicit kernel mapping is given in terms of the nonlinear parameter identification procedure, in which the model is transformed from the nonlinear-in-nature to the linear-in-parameter. Numerical and real cases are carried out finally to demonstrate the effectiveness and generalization ability of the proposed method. |
| format | Article |
| id | doaj-art-b0507aff1e494720944b88dcf905dc0c |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-b0507aff1e494720944b88dcf905dc0c2025-02-03T05:44:10ZengWileyComplexity1076-27871099-05262017-01-01201710.1155/2017/81976028197602Nonlinear System Identification Using Quasi-ARX RBFN Models with a Parameter-Classified SchemeLan Wang0Yu Cheng1Jinglu Hu2Jinling Liang3Abdullah M. Dobaie4Wuxi Institute of Technology, Wuxi 214121, ChinaAnt Financial Services Group, Hangzhou 310099, ChinaGraduate School of Information, Production and Systems, Waseda University, Kitakyushu 808-0135, JapanSchool of Mathematics, Southeast University, Nanjing 210096, ChinaDepartment of Electrical and Computer Engineering, King Abdulaziz University, Jeddah 21589, Saudi ArabiaQuasi-linear autoregressive with exogenous inputs (Quasi-ARX) models have received considerable attention for their usefulness in nonlinear system identification and control. In this paper, identification methods of quasi-ARX type models are reviewed and categorized in three main groups, and a two-step learning approach is proposed as an extension of the parameter-classified methods to identify the quasi-ARX radial basis function network (RBFN) model. Firstly, a clustering method is utilized to provide statistical properties of the dataset for determining the parameters nonlinear to the model, which are interpreted meaningfully in the sense of interpolation parameters of a local linear model. Secondly, support vector regression is used to estimate the parameters linear to the model; meanwhile, an explicit kernel mapping is given in terms of the nonlinear parameter identification procedure, in which the model is transformed from the nonlinear-in-nature to the linear-in-parameter. Numerical and real cases are carried out finally to demonstrate the effectiveness and generalization ability of the proposed method.http://dx.doi.org/10.1155/2017/8197602 |
| spellingShingle | Lan Wang Yu Cheng Jinglu Hu Jinling Liang Abdullah M. Dobaie Nonlinear System Identification Using Quasi-ARX RBFN Models with a Parameter-Classified Scheme Complexity |
| title | Nonlinear System Identification Using Quasi-ARX RBFN Models with a Parameter-Classified Scheme |
| title_full | Nonlinear System Identification Using Quasi-ARX RBFN Models with a Parameter-Classified Scheme |
| title_fullStr | Nonlinear System Identification Using Quasi-ARX RBFN Models with a Parameter-Classified Scheme |
| title_full_unstemmed | Nonlinear System Identification Using Quasi-ARX RBFN Models with a Parameter-Classified Scheme |
| title_short | Nonlinear System Identification Using Quasi-ARX RBFN Models with a Parameter-Classified Scheme |
| title_sort | nonlinear system identification using quasi arx rbfn models with a parameter classified scheme |
| url | http://dx.doi.org/10.1155/2017/8197602 |
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