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Flexible adaptive filtering by minimization of error entropy bound and its application to system identification

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2 Author(s)
Xi-Lin Li ; Univ. of Maryland Baltimore County, Baltimore, MD, USA ; Adali, T.

It has been shown that using minimum error entropy as the cost function leads to important performance gains in adaptive filtering, especially when the Gaussianity assumptions on the error distribution do not hold. In this paper, we show that by using the entropy bound rather than the entropy, we can derive an efficient algorithm for supervised training. We demonstrate its effectiveness by a system identification problem using a generalized Gaussian noise model.

Published in:

Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on

Date of Conference:

14-19 March 2010