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.
We present methods to systematically design a feedforward neural-network detector from the knowledge of the channel characteristics. Its performance is compared with the conventional linear equalizer in a magnetic recording channel suffering from signal-dependent noise and nonlinear intersymbol interference. The superiority of the nonlinear schemes are clearly observed in all cases studied, especially in the presence of severe nonlinearity and noise. We also show that the decision boundaries formed by a theoretically derived neural-network classifier are geometrically close to those of a neural network trained by the backpropagation algorithm. The approach in this work is suitable for quantifying the gain in using a neural-network method as opposed to linear methods in the classification of noisy patterns.
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