Networks with trainable amplitude of activation functions
Neural Networks
Plant identification via adaptive combination of transversal filters
Signal Processing - Signal processing in UWB communications
Adaptive Filters
Uniformly Improving the CramÉr-Rao Bound and Maximum-Likelihood Estimation
IEEE Transactions on Signal Processing
Mean-square performance of a convex combination of two adaptive filters
IEEE Transactions on Signal Processing
An Affine Combination of Two LMS Adaptive Filters—Transient Mean-Square Analysis
IEEE Transactions on Signal Processing
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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 using an output multiplicative factor. We give theoretical results that show that the proposed configuration is able to provide a convenient bias versus variance tradeoff, leading to reductions in the filter mean-square error, especially in situations with a low signal-to-noise ratio (SNR). After reinterpreting the biased estimator as the combination of the original filter and a filter with constant output equal to 0, we propose practical schemes to adaptively adjust the multiplicative factor. Experiments are carried out for the normalized least-mean-squares (NLMS) adaptive filter, improving its mean-square performance in stationary situations and during the convergence phase.