@inproceedings{5da4f5d358174f098950a8d9a3fdeff4,
title = "Equalization and the impulsive MOOSE: fast adaptive signal recovery in very heavy tailed noise",
abstract = "We combine Weighted Order Statistic (WOS) filters as efficient reconstructors of signals with extremely impulsive noise with the deconvolution capabilities of artificial neurons in a fast, memory efficient way. The Multi-rate Optimising Order Statistics Equalizer (MOOSE) neural architecture combines a FIR WOS Hybrid (FWH) pre-filter and a neural deconvolver with differing sampling rates. These cooperate synergistically after training. In the simulated equalization of a BAM channel with high ISI and additive noise. with Gaussian and frequent, high amplitude impulsive components. the linear MOOSE proved far superior to linear filtering. MOOSE computational complexity is far below that of the comparably performing L1 filter.",
author = "Eugene Dubossarsky and Osborn, {Thomas R.} and Sam Reisenfeld",
year = "1997",
language = "English",
isbn = "0805829008",
series = "INTERNATIONAL NEURAL NETWORKS SOCIETY SERIES",
publisher = "Lawrence Erlbaum Associates",
pages = "232--240",
editor = "Joshua Alspector and Rodney Goodman and Brown, {Timothy X}",
booktitle = "Proceedings of the international workshop on applications of neural networks to telecommunications 3",
note = "3rd International Workshop on the Application of Neural Networks to Telecommunications (IWANNT 97) ; Conference date: 09-06-1997 Through 11-06-1997",
}