Abstract
As recording density rises read signals are increasingly distorted by nonlinear intersymbol interference (ISI). Against this background an artificial neural network with a new decision making scheme has been set up and trained to work as a detector. Tests have been performed with experimentally captured read signals from a modified disk drive with magneto-resistive (MR) read heads. In comparison with multi-level decision feedback equalization (MDFE) the detection results show superior performance at extremely high linear recording densities. An error rate of 4.10/sup -6/ has been achieved at a user density D/sub u/=3.5. We describe the architecture and the training procedure of the neural network and present detection results.
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