Multi-model approach for nonlinear system identification by EM algorithm
The problem of system identification for nonlinear system is studied in this paper by using EM algorithm, and a stochastic scheduling parameter which follows a Markov jump process is considered. First, multi-model approach is addressed to describe the nonlinear process, where each linear parameter s...
| Main Authors: | , , |
|---|---|
| Format: | Journal Article |
| Published: |
ICIC International
2017
|
| Online Access: | http://hdl.handle.net/20.500.11937/56861 |
| _version_ | 1848759955059900416 |
|---|---|
| author | Wei, J. Yin, YanYan Liu, F. |
| author_facet | Wei, J. Yin, YanYan Liu, F. |
| author_sort | Wei, J. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The problem of system identification for nonlinear system is studied in this paper by using EM algorithm, and a stochastic scheduling parameter which follows a Markov jump process is considered. First, multi-model approach is addressed to describe the nonlinear process, where each linear parameter system is represented by an auto regressive exogenous model, and then, EM algorithm is used to do estimation with the help of stochastic scheduling parameter. A simulation example is given to illustrate the effectiveness of the approach proposed. |
| first_indexed | 2025-11-14T10:08:06Z |
| format | Journal Article |
| id | curtin-20.500.11937-56861 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:08:06Z |
| publishDate | 2017 |
| publisher | ICIC International |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-568612018-01-11T07:34:55Z Multi-model approach for nonlinear system identification by EM algorithm Wei, J. Yin, YanYan Liu, F. The problem of system identification for nonlinear system is studied in this paper by using EM algorithm, and a stochastic scheduling parameter which follows a Markov jump process is considered. First, multi-model approach is addressed to describe the nonlinear process, where each linear parameter system is represented by an auto regressive exogenous model, and then, EM algorithm is used to do estimation with the help of stochastic scheduling parameter. A simulation example is given to illustrate the effectiveness of the approach proposed. 2017 Journal Article http://hdl.handle.net/20.500.11937/56861 10.24507/icicel.11.09.1461 ICIC International restricted |
| spellingShingle | Wei, J. Yin, YanYan Liu, F. Multi-model approach for nonlinear system identification by EM algorithm |
| title | Multi-model approach for nonlinear system identification by EM algorithm |
| title_full | Multi-model approach for nonlinear system identification by EM algorithm |
| title_fullStr | Multi-model approach for nonlinear system identification by EM algorithm |
| title_full_unstemmed | Multi-model approach for nonlinear system identification by EM algorithm |
| title_short | Multi-model approach for nonlinear system identification by EM algorithm |
| title_sort | multi-model approach for nonlinear system identification by em algorithm |
| url | http://hdl.handle.net/20.500.11937/56861 |