Gradient-based iterative parameter estimation algorithms for dynamical systems from observation data

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Abstract

It is well-known that mathematical models are the basis for system analysis and controller design. This paper considers the parameter identification problems of stochastic systems by the controlled autoregressive model. A gradient-based iterative algorithm is derived from observation data by using the gradient search. By using the multi-innovation identification theory, we propose a multi-innovation gradient-based iterative algorithm to improve the performance of the algorithm. Finally, a numerical simulation example is given to demonstrate the effectiveness of the proposed algorithms.

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Ding, F., Pan, J., Alsaedi, A., & Hayat, T. (2019). Gradient-based iterative parameter estimation algorithms for dynamical systems from observation data. Mathematics, 7(5). https://doi.org/10.3390/math7050428

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