Simplification of neural network equalizer for perpendicular magnetic recording

Hisashi Osawa, Toshimasa Shimizu, Takumi Nakaoka, Yoshihiro Okamoto, Hidetoshi Saito, Hiroaki Muraoka, Yoshihisa Nakamura

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

In this paper, we examine neural network equalization in a perpendicular magnetic recording channel with jitter medium noise and MR nonlinear distortion. First, we propose simplifying a neural network equalizer by using a hybrid genetic algorithm. Next, we determine the bit error rate of a PR2ML method provided with the simplified neural network equalizer and compare it to those of a conventional neural network equalizer and a transversal filter equalizer. The results were improvements of about 0.8 and 2.0 dB in the SNR of the PR2ML method using the simplified neural network equalizer compared to the conventional neural network equalizer and the transversal filter equalizer, respectively.

Original languageEnglish
Pages (from-to)19-27
Number of pages9
JournalElectronics and Communications in Japan, Part II: Electronics (English translation of Denshi Tsushin Gakkai Ronbunshi)
Volume89
Issue number2
DOIs
Publication statusPublished - 2006 Feb 1

Keywords

  • Genetic algorithm
  • Jitter medium noise
  • MR nonlinear distortion
  • Neural network equalizer
  • Perpendicular magnetic recording

ASJC Scopus subject areas

  • Physics and Astronomy(all)
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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