Iterative Wiener design of adaptation laws with constant gains
نویسندگان
چکیده
Abstract: We present a method for optimizing adaptation laws that are generalizations of the LMS algorithm. Timevarying parameters of linear regression models are estimated in situations where the regressors are stationary or have slowly time-varying properties. The parameter variations are modeled as ARIMA-processes and the aim is to use such prior information to provide high performance filtering, prediction or fixed lag smoothing estimates for arbitrary lags. The method is based on a novel signal transformation that recasts the algorithm design problem into a Wiener design.
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تاریخ انتشار 2001