Fast fixed-point neural blind-deconvolution algorithm

IEEE Transactions on Neural Networks
Simone Fiori

Abstract

The aim of this letter is to introduce a new blind-deconvolution algorithm based on fixed-point optimization of a "Bussgang"-type cost function. The cost function relies on approximate Bayesian estimation achieved by an adaptive neuron. The main feature of the presented algorithm is fast convergence that guarantees good deconvolution performances with limited computational demand as compared with algorithms of the same class.

References

Nov 1, 1995·Neural Computation·A J Bell, T J Sejnowski
Feb 2, 2008·IEEE Transactions on Neural Networks·P A Regalia, E Kofidis

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Citations

Apr 15, 2005·Neural Computation·Simone Fiori
May 29, 2007·IEEE Transactions on Neural Networks·Dalong LiSteven Simske
Jan 1, 2012·IEEE Transactions on Neural Networks and Learning Systems·Simone Fiori
Mar 24, 2011·IEEE Transactions on Neural Networks·Simone Fiori
Oct 11, 2011·IEEE Transactions on Neural Networks·Simone Fiori
Apr 6, 2007·Journal of Neural Engineering·F LotteB Arnaldi
Dec 17, 2014·Physiological Measurement·Marnie L LamprechtPhilip I Terrill

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