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Adaptive filters can improve their performance by exploiting the well-known tradeoff between bias and variance of the estimated solution. In a previous work, a scheme for adaptively biasing the filter weights was introduced, multiplying the output of a filter of any kind by a shrinking factor α ∈ [0, 1]. With an appropriate value α, such a scheme can reduce the steady-state error, especially for low...
It is a well-known result of estimation theory that biased estimators can outperform unbiased ones in terms of expected quadratic error. In steady state, many adaptive filtering algorithms offer an unbiased estimation of both the reference signal and the unknown true parameter vector. In this correspondence, we propose a simple yet effective scheme for adaptively biasing the weights of adaptive filters...
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