Gradient-Based Iterative Parameter Estimation Algorithms for Dynamical Systems from Observation Data

Feng Ding, Jian Pan, Ahmed Alsaedi and Tasawar Hayat
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Feng Ding: School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China
Jian Pan: School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China
Ahmed Alsaedi: Department of Mathematics, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Tasawar Hayat: Department of Mathematics, King Abdulaziz University, Jeddah 21589, Saudi Arabia

Mathematics, 2019, vol. 7, issue 5, 1-15

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.

Keywords: parameter estimation; iterative algorithm; gradient search; multi-innovation identification; stochastic system (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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