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Adaptive identification of nonlinear systems with application to chaotic communications

机译:非线性系统的自适应辨识及其在混沌通信中的应用

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This paper studies the nonlinear system identification problem in a noisy environment using an adaptive algorithm. In particular, nonlinear systems of the polynomial type are considered here. An improved least squares (ILS) objective function is used to reduce the estimation bias caused by measurement noise. Based on this ILS criterion, a novel adaptive filter is developed to track a time-varying polynomial system. Numerical simulations showed that the proposed adaptive algorithm was superior to the conventional identification technique. We applied this new adaptive filter to demodulate the signals of transmission in a chaotic multiuser spread spectrum (SS) communication system. It was observed that the new approach was effective in demodulating a SS signal, even at low signal-to-noise ratios (SNR's).
机译:本文采用自适应算法研究了嘈杂环境中的非线性系统辨识问题。在此尤其要考虑多项式类型的非线性系统。改进的最小二乘(ILS)目标函数用于减少由测量噪声引起的估计偏差。基于此ILS准则,开发了一种新颖的自适应滤波器来跟踪时变多项式系统。数值仿真表明,所提出的自适应算法优于传统的识别技术。我们应用了这种新的自适应滤波器来解调混沌多用户扩频(SS)通信系统中的传输信号。据观察,即使在低信噪比(SNR)的情况下,新方法也能有效解调SS信号。

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