Parameter estimation algorithms for dynamical response signals based on the multi-innovation theory and the hierarchical principle
In this study, the authors consider the parameter estimation problem of the response signal from a highly non-linear dynamical system. The step response experiment is taken for generating the measured data. Considering the stochastic disturbance in the industrial process and using the gradient searc...
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| Veröffentlicht in: | IET signal processing Jg. 11; H. 2; S. 228 - 237 |
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| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
The Institution of Engineering and Technology
01.04.2017
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| ISSN: | 1751-9675, 1751-9683, 1751-9683 |
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| Abstract | In this study, the authors consider the parameter estimation problem of the response signal from a highly non-linear dynamical system. The step response experiment is taken for generating the measured data. Considering the stochastic disturbance in the industrial process and using the gradient search, a multi-innovation stochastic gradient algorithm is proposed through expanding the scalar innovation into an innovation vector in order to obtain more accurate parameter estimates. Furthermore, a hierarchical identification algorithm is derived by means of the decomposition technique and interaction estimation theory. Regarding to the coupled parameter problem between subsystems, the authors put forward the scheme of replacing the unknown parameters with their previous parameter estimates to realise the parameter estimation algorithm. Finally, several examples are provided to access and compare the behaviour of the proposed identification techniques. |
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| AbstractList | In this study, the authors consider the parameter estimation problem of the response signal from a highly non‐linear dynamical system. The step response experiment is taken for generating the measured data. Considering the stochastic disturbance in the industrial process and using the gradient search, a multi‐innovation stochastic gradient algorithm is proposed through expanding the scalar innovation into an innovation vector in order to obtain more accurate parameter estimates. Furthermore, a hierarchical identification algorithm is derived by means of the decomposition technique and interaction estimation theory. Regarding to the coupled parameter problem between subsystems, the authors put forward the scheme of replacing the unknown parameters with their previous parameter estimates to realise the parameter estimation algorithm. Finally, several examples are provided to access and compare the behaviour of the proposed identification techniques. |
| Author | Ding, Feng Xu, Ling |
| Author_xml | – sequence: 1 givenname: Ling surname: Xu fullname: Xu, Ling organization: 2School of Internet of Things Technology, Wuxi Vocational Institute of Commerce, Wuxi 214153, People's Republic of China – sequence: 2 givenname: Feng surname: Ding fullname: Ding, Feng email: fding@jiangnan.edu.cn organization: 1Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, People's Republic of China |
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| Keywords | estimation theory signal processing parameter estimation algorithm decomposition technique dynamical response signal industrial process multiinnovation stochastic gradient algorithm interaction estimation theory nonlinear dynamical system scalar innovation innovation vector gradient search parameter estimation stochastic disturbance stochastic processes gradient methods multiinnovation theory hierarchical principle hierarchical identification algorithm |
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| SubjectTerms | Algorithms decomposition technique dynamical response signal Dynamical systems Estimates estimation theory gradient methods gradient search hierarchical identification algorithm hierarchical principle industrial process Innovation innovation vector interaction estimation theory multiinnovation stochastic gradient algorithm multiinnovation theory nonlinear dynamical system Parameter estimation parameter estimation algorithm Parameters Research Article scalar innovation Scalars signal processing stochastic disturbance stochastic processes Stochasticity |
| Title | Parameter estimation algorithms for dynamical response signals based on the multi-innovation theory and the hierarchical principle |
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