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

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

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 t...

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Bibliographic Details
Main Authors: Feng Ding, Jian Pan, Ahmed Alsaedi, Tasawar Hayat
Format: Article
Language:English
Published: MDPI AG 2019-05-01
Series:Mathematics
Subjects:
parameter estimation
iterative algorithm
gradient search
multi-innovation identification
stochastic system
Online Access:https://www.mdpi.com/2227-7390/7/5/428
Description
Summary: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.
ISSN:2227-7390

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