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