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Optimal neuro-fuzzy equalizers for nonlinear channels of the perpendicular magnetic recording system
Author(s)
Wongsathan, Rati
Date Issued
January 1, 2021
Type
Article
Abstract
Nonlinear distortions caused by partial erasure and nonlinear transition shifts interacting with inter-symbol interference, are a major hindrance to data storage systems, since they degrade detector performance. This work aims to design and optimize the neuro-fuzzy equalizer (NFE) using the multi-objective genetic algorithm (MOGA) to detect nonlinear high-density magnetic recording (MR) channels. Through the GA-assisted back-propagation algorithm and least mean square optimization, the complexity in terms of decision rules is reduced by 25% and significantly provides 65% lower signal processing computation. When applied to the perpendicular (MR) system, the proposed NFE outperforms existing equalizers such as the neural network-based equalizer, fuzzy logic equalizer, and conventional NFE for the Volterra and jitter media noise channels using 1-3 dB and 1.5-3.5 dB signalto-noise ratio gains at the bit-error-rate of 10−4, respectively. Furthermore, compared to the other models, the NFE provides a more effective output mean square error performance for retrieving the original bit data.
Citation
Ecti Transactions on Electrical Engineering Electronics and Communications, 19(2), 190-199, 2021