Title: A novel maximum likelihood-based stochastic gradient algorithm for Hammerstein nonlinear systems with coloured noise

Authors: Yan Pu; Jing Chen

Addresses: School of Internet of Things Engineering, School of Science, Jiangnan University, Wuxi 214122, China ' School of Internet of Things Engineering, School of Science, Jiangnan University, Wuxi 214122, China

Abstract: This paper proposes a novel maximum likelihood based stochastic gradient algorithm for Hammerstein nonlinear systems with coloured noise. The unknown noises in the information vector are replaced by their estimates, and then the parameters can be obtained by using the proposed algorithm through the noise estimates. Compared with the maximum likelihood-based recursive least squares algorithm, the proposed algorithm has less computation burden. Furthermore, the performance of the proposed algorithm is analysed and compared using a simulation example.

Keywords: system identification; stochastic gradient algorithm; recursive least squares algorithm; maximum likelihood; Hammerstein system.

DOI: 10.1504/IJMIC.2019.101968

International Journal of Modelling, Identification and Control, 2019 Vol.32 No.1, pp.23 - 29

Received: 08 Oct 2018
Accepted: 29 Nov 2018
Published online: 02 Sep 2019 *

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