Identification of Dual-Rate Sampled Hammerstein Systems with a Piecewise-Linear Nonlinearity Using the Key Variable Separation Technique

Abstract

Abstract:The identification difficulties for a dual-rate Hammerstein system lie in two aspects. First, the identification model of the system contains the products of the parameters of the nonlinear block and the linear block, and a standard least squares method cannot be directly applied to the model; second, the traditional single-rate discrete-time Hammerstein model cannot be used as the identification model for the dual-rate sampled system. In order to solve these problems, by combining the polynomial transformation technique with the key variable separation technique, this paper converts the Hammerstein system into a dual-rate linear regression model about all parameters (linear-in-parameter model) and proposes a recursive least squares algorithm to estimate the parameters of the dual-rate system. The simulation results verify the effectiveness of the proposed algorithm.

Gradient-Based Iterative Identification for Wiener Nonlinear Dynamic Systems with Moving Average Noises

“…Block-oriented nonlinear systems can be commonly divided into Hammerstein systems and Wiener systems [1][2][3]. Hammerstein systems consist of a linear block following a static nonlinear block [4][5][6]. Wiener systems are composed of a linear block preceding a static nonlinear block [7][8][9].…”

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

“…The state of the art in designing, analyzing and implementing identification algorithms for block-oriented nonlinear systems were well summarized in a recent book by Giri and Bai [3]. Depending on the location of the static nonlinear component, block-oriented models can be classified into the Hammerstein model, the Wiener model and the Hammerstein-Wiener model [4][5][6]. The Hammerstein model represents a class of input nonlinear systems, where the nonlinear block is prior to the linear one.…”