Data storage channel equalization using neural networks

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Unlike in many communication channels, the read signals in thin-film magnetic recording channels are corrupted by non-Gaussian, data-dependent noise and nonlinear distortions. In this work we use feedforward neural networks-a multilayer perceptron (MLP) and its simplified variations-to equalize these signals, We demonstrate that they improve the performance of data recovery schemes in comparison with conventional equalizers, The variations of the MLP equalizer are suitable for the low complexity VLSI implementation required in data storage systems. We also present a novel training criterion designed to reduce the probability of error for the recovered digital data, The results were obtained both from experimental data and from a software recording channel simulator using thin-film disk and magnetoresistive head models.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
1997-09
Language
English
Article Type
Article
Keywords

DECISION FEEDBACK EQUALIZATION; RECORDING-SYSTEMS; PARTIAL-RESPONSE; FILM MEDIA; PERFORMANCE; NOISE; DENSITY; PRML; NONLINEARITIES; MODEL

Citation

IEEE TRANSACTIONS ON NEURAL NETWORKS, v.8, no.5, pp.1037 - 1048

ISSN
1045-9227
DOI
10.1109/72.623206
URI
http://hdl.handle.net/10203/75638
Appears in Collection
EE-Journal Papers(저널논문)
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