Parameter estimation with scarce measurements |
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| Authors: | Feng Ding Guangjun Liu Xiaoping P. Liu[Author vitae] |
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| Affiliation: | aKey Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, China;bControl Science and Engineering Research Center, Jiangnan University, Wuxi 214122, China;cDepartment of Aerospace Engineering, Ryerson University, Toronto, Canada M5B 2K3;dDepartment of Systems and Computer Engineering, Carleton University, Ottawa, Canada K1S 5B6 |
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| Abstract: | In this paper, the problems of parameter estimation are addressed for systems with scarce measurements. A gradient-based algorithm is derived to estimate the parameters of the input–output representation with scarce measurements, and the convergence properties of the parameter estimation and unavailable output estimation are established using the Kronecker lemma and the deterministic version of the martingale convergence theorem. Finally, an example is provided to demonstrate the effectiveness of the proposed algorithm. |
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| Keywords: | Parameter estimation Recursive identification Stochastic gradient Missing data Multi-innovation identification |
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