Tim Erdmann, Stefan Zecevic, et al.
ACS Spring 2024
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. © 1997 IEEE.
Tim Erdmann, Stefan Zecevic, et al.
ACS Spring 2024
Salvatore Certo, Anh Pham, et al.
Quantum Machine Intelligence
Gaku Yamamoto, Hideki Tai, et al.
AAMAS 2008
Ken C.L. Wong, Satyananda Kashyap, et al.
Pattern Recognition Letters