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Nonlinear Modeling of Super-RENS System Using a Neural Networks  

Seo, Man-Jung (School of Electronic Engineering, Soongsil University)
Im, Sung-Bin (School of Electronic Engineering, Soongsil University)
Lee, Jae-Jin (School of Electronic Engineering, Soongsil University)
Publication Information
Abstract
Recently, various recording technologies are studied for optical data storage. After standardization of BD (Blue-ray Disc) and HD-DVD (High-Definition Digital Versatile Disc), the industry is looking for a suitable technology for next generation optical data storage. Super-RENS (Super-resolution near field structure) technique, which is capable of compatibility with other systems, is one of next optical data storage. In this paper, we analyze the nonlinearity of Super-RENS read-out signal through the bicoherence test, which uses HOS (Higher-Order Statistics) and apply neural networks for nonlinear modeling. The model structure considered in this paper is the NARX (Nonlinear AutoRegressive eXogenous) model. The experiment results indicate that the read-out signals have nonlinear characteristics. In addition, it verified the possibility that neural networks can be utilized for nonlinear modeling of Super-RENS systems.
Keywords
Super-RENS; neural network; bicoherence; nonlinearity; NARX;
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