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Decomposition-Based Gradient Estimation Algorithms for Multivariate Equation-Error Autoregressive Systems Using the Multi-innovation Theory

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Abstract

This paper studies the parameter estimation algorithms of multivariate equation-error autoregressive systems. By using the decomposition technique, the multivariate equation-error autoregressive system is decomposed into two subsystems, and a decomposition-based generalized stochastic gradient algorithm is deduced for estimating the parameters of these two subsystems. In order to further improve the parameter accuracy, a decomposition-based multi-innovation generalized stochastic gradient algorithm is developed by means of the multi-innovation theory. The simulation results confirm that these two algorithms are effective.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 61273194) and the 111 Project (B12018).

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Authors and Affiliations

  1. Key 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

    Ping Ma & Feng Ding

  2. Nonlinear Analysis and Applied Mathematics (NAAM) Research Group, Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

    Feng Ding, Ahmed Alsaedi & Tasawar Hayat

  3. Department of Mathematics, Quaid-I-Azam University, Islamabad, 44000, Pakistan

    Tasawar Hayat

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  1. Ping Ma
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  4. Tasawar Hayat
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Correspondence to Feng Ding.

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Ma, P., Ding, F., Alsaedi, A. et al. Decomposition-Based Gradient Estimation Algorithms for Multivariate Equation-Error Autoregressive Systems Using the Multi-innovation Theory. Circuits Syst Signal Process 37, 1846–1862 (2018). https://doi.org/10.1007/s00034-017-0644-0

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