Combining the Best Linear Approximation and Dimension Reduction to Identify the Linear Blocks of Parallel Wiener Systems
نویسندگان
چکیده
A Wiener model is a fairly simple, well known, and often used nonlinear block-oriented black-box model. A possible generalization of the class of Wiener models lies in the parallel Wiener model class. This paper presents a method to estimate the linear time-invariant blocks of such parallel Wiener models from input/output data only. The proposed estimation method combines the knowledge obtained by estimating the best linear approximation of a nonlinear system with a dimension reduction method to estimate the linear time-invariant blocks present in the model. The estimation of the static nonlinearity is fairly easy once the linear blocks are known.
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