Abstract
The authors introduce a new class of filters, the generalized feedforward structures, that combine attractive properties of the moving average (MA) filters for adaptation (i.e. fast algorithms, trivial stability) with some of the power of autoregressive moving average (ARMA) filters (i.e. decoupling of the length of the impulse response with filter order). Preliminary results show that this class of filters is much more efficient than conventional MA filters (i.e. for a given minimum mean square error (MSE) the filter order is much smaller). The authors have extended the Wiener-Hopf solution for this class of filters and have developed some design tools. The generalized feedforward structures accept Widrow's adaptive linear combiner as a special case. An identification example is presented.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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