Abstract
This paper considers the parameter identification problem of block-oriented Hammerstein nonlinear systems with time-delay. Firstly, we adopt the data filtering technique to transform the identification model so that all the parameters will be separated in the resulting identification model which has no redundant parameters. Secondly, a multi-innovation stochastic gradient algorithm is used to estimate the system parameters. The proposed method has high computational efficiency and good accuracy. Simulation results are presented to demonstrate the effectiveness of the proposed algorithm.
Highlights
This paper considers the parameter identification problem of block-oriented Hammerstein nonlinear systems with time-delay
Parameter identification means estimating the parameters of partially unknown systems based on noisy observations, which is the foundation of many issues such as signal processing, system identification and system control[1,2,3,4,5]
The Hammerstein system consists of a nonlinear block plus a linear block, while the Wiener systems consists of a linear dynamic system block followed by a static nonlinearity block
Summary
Parameter identification means estimating the parameters of partially unknown systems based on noisy observations, which is the foundation of many issues such as signal processing, system identification and system control[1,2,3,4,5]. Where u(t) and y(t) are the system input and output, and v(t) is a white noise with zero mean, uf (t) is a nonlinear function, A(z) and B(z) are the polynomials in the unit backward shift operator z−1y(t) = y(t − 1) which contains the parameter vectors to be identified. On the basis of the work in[16], a multi-innovation stochastic gradient algorithm is proposed to identify the parameters of nonlinear systems with nonlinear systems which include the products of the original system parameters and time-delay. We derive a stochastic gradient algorithm for the nonlinear systems with time-delay to identify the parameter vector θ. The steps involved in computing the parameter estimate θ as t increases using the MI-SG algorithm is summarized as follows: 1.
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