Multivariate Shannon's entropy for adaptive IIR filtering via kernel density estimators. Issue 15 (1st July 2019) - Fantinato, D.G.; Silva, D.G.; Attux, R.; Neves, A.
Abstract : In supervised infinite impulse response adaptive filtering, approximate gradient‐based approaches are the usual option among optimisation methods. When based on the mean squared error (MSE) criterion, however, these approaches may present biased solutions in noisy scenarios. In that sense, instead of the MSE, the authors propose the use of Shannon's error entropy, an information theoretic learning criterion, which is able to extract higher order statistics from the underlying signals. In particular, a multivariate entropy definition is considered, which is applied to derive a Recursive Prediction Error‐based algorithm. The performance analyses are carried out in the context of the supervised channel equalisation problem, with results very favourable to the proposal, in high and low noise level environments.