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• W2136316997 endingPage "1048" @default.
• W2136316997 startingPage "1037" @default.
• W2136316997 abstract "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 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." @default.
• W2136316997 created "2016-06-24" @default.
• W2136316997 creator A5046069388 @default.
• W2136316997 creator A5050809734 @default.
• W2136316997 date "1997-09-01" @default.
• W2136316997 modified "2023-09-27" @default.
• W2136316997 title "Data storage channel equalization using neural networks" @default.
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• W2136316997 doi "https://doi.org/10.1109/72.623206" @default.
• W2136316997 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/18255707" @default.
• W2136316997 hasPublicationYear "1997" @default.
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