Abstract
In this paper, a framework for selecting input sequences is presented for non-parametric nonlinear system identification so that interaction terms can be identified separably. In a sense, the approach decomposes a general difficult high dimensional nonlinear identification problem into a number of problems that are lower dimensionable. Corresponding identification algorithms are proposed and shown to be convergent.
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