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Auxiliary Model Based Multi-Innovation Stochastic Gradient Identification Algorithm for Periodically Non-Uniformly Sampled-Data Hammerstein Systems

by Li Xie 1,2,* and Huizhong Yang 1,2
1
Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, China
2
School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China
*
Author to whom correspondence should be addressed.
Algorithms 2017, 10(3), 84; https://doi.org/10.3390/a10030084
Received: 12 May 2017 / Revised: 7 July 2017 / Accepted: 19 July 2017 / Published: 31 July 2017
Due to the lack of powerful model description methods, the identification of Hammerstein systems based on the non-uniform input-output dataset remains a challenging problem. This paper introduces a time-varying backward shift operator to describe periodically non-uniformly sampled-data Hammerstein systems, which can simplify the structure of the lifted models using the traditional lifting technique. Furthermore, an auxiliary model-based multi-innovation stochastic gradient algorithm is presented to estimate the parameters involved in the linear and nonlinear blocks. The simulation results confirm that the proposed algorithm is effective and can achieve a high estimation performance. View Full-Text
Keywords: non-uniform sampling; Hammerstein system; parameter estimation; multi-innovation theory; stochastic gradient algorithm non-uniform sampling; Hammerstein system; parameter estimation; multi-innovation theory; stochastic gradient algorithm
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Xie, L.; Yang, H. Auxiliary Model Based Multi-Innovation Stochastic Gradient Identification Algorithm for Periodically Non-Uniformly Sampled-Data Hammerstein Systems. Algorithms 2017, 10, 84.

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