A novel maximum likelihood-based stochastic gradient algorithm for Hammerstein nonlinear systems with coloured noise. (29th August 2019) - Pu, Yan; Chen, Jing
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.