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Performance improvement of chaos-based communications by using neural filtering

机译:通过使用神经滤波来表现基于混沌的通信的性能

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In recent years chaos in communication systems have achieved quite outstanding outcome. Chaotic communication signals are spread spectrum signals, which utilize large bandwidth and have low power spectrum density. In traditional communication systems, the analog sinusoid waveforms are linear. In contrast in chaotic communication systems the waveforms are nonlinear. Due to nonlinear, unsteady, nonperiodic and deterministic characteristic of chaos it has numerous opportunities to develop communication research. In this paper, we have described a chaotic communication schemes which contains three important parts named as chaos generation, masking of signal and filtering of recovered signal. Lorenz attractor is used for chaos generation whereas an artificial neural network based on back propagation is used as filter named as neural filter to reduce the noise from the recovered signal. From the simulated result it has been cleared that neural filtering provides good peak signal to noise ratio (PSNR) in both chaotic and conventional communication system.
机译:近年来,通信系统中的混乱取得了相当突出的结果。混沌通信信号是扩频信号,其利用大带宽并具有低功率谱密度。在传统通信系统中,模拟正弦波形是线性的。相比之下,波形是非线性的。由于非线性,不稳定,非星期性和确定性的混乱特征,它具有许多发展通信研究的机会。在本文中,我们描述了一种混沌通信方案,其包含名为Chaos生成的三个重要部件,信号和恢复信号的过滤。 Lorenz吸引仪用于混沌生成,而基于反向传播的人工神经网络用作名为神经滤波器的滤波器,以减少来自恢复信号的噪声。从模拟结果,已经清除了神经滤波在混沌和传统通信系统中提供良好的峰值信噪比(PSNR)。

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