Data Storage Channel Equalization Using Neural Networks  

Oleh:

Moon, J. ; Nair, S. K.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 8 no. 5 (1997), page 1037-1048.
Topik: storage; data storage; channel equalization; neural networks

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.2 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

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.

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