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Gradient-based Parameter Estimation for a Nonlinear Exponential Autoregressive Time-series Model by Using the Multi-innovation

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  • Control Theory and Applications
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Abstract

The parameter estimation methods for the nonlinear exponential autoregressive model are investigated in this paper. We develop a forgetting factor gradient parameter estimation algorithm for improving the estimation accuracy. For the purpose of improving the identification accuracy further, a forgetting factor multi-innovation stochastic gradient algorithm is derived by using the multi-innovation theory. The effectiveness of the proposed algorithms is proved by a simulation example.

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Authors and Affiliations

  1. School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan, 430068, China

    Jian Pan, Yuqing Liu & Jun Shu

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  1. Jian Pan
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  2. Yuqing Liu
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  3. Jun Shu
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Correspondence to Jian Pan.

Additional information

Jian Pan was born in Wuhan, China. He received his B.Sc. degree from Hubei University of Technology, Wuhan, China in 1984. He has been a Professor in the School of Electrical and Electronic Engineering, Hubei University of Technology. His research interests include control science and engineering, computer control systems, and power electronics.

Yuqing Liu was born in Xiangtan, Hunan Province, China. She received her B.Sc. degree from the Hunan Institute of Science and Technology (Yueyang, China) in 2019. She is now a master student at the Huibei University of Technology, Wuhan, China. Her research interests include nonlinear system identification and control theory.

Jun Shu was born in Wuhan, China. He received his B.Sc. degree in 2009 and an M.Sc. degree from Hubei University of Technology, Wuhan, China. He has been an Associate Professor at Hubei University of Technology. His research interests include intelligent control and process control.

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This work was supported by the National Natural Science Foundation of China (No. 61571182, 61273192).

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Pan, J., Liu, Y. & Shu, J. Gradient-based Parameter Estimation for a Nonlinear Exponential Autoregressive Time-series Model by Using the Multi-innovation. Int. J. Control Autom. Syst. 21, 140–150 (2023). https://doi.org/10.1007/s12555-021-1018-8

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