Identification of linear systems driven by chaotic signals using nonlinear prediction
Identification of linear systems driven by chaotic signals using nonlinear prediction
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DOI：
10.1109/81.983865
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发表时间：
2002-08
影响因子：
5.1
通讯作者：
Zhiwen Zhu;H. Leung
中科院分区：
文献类型：
--
作者：
Zhiwen Zhu;H. Leung
研究了离散混沌信号驱动下线性系统的盲辨识问题。基于混沌信号的短期可预测性，提出了一种有效的逆滤波辨识方法--最小非线性预测误差（MNPE）法。非线性预测误差（NPE）准则被用作逆滤波的目标函数。结果表明，所提出的MNPE逆滤波方法可以准确地识别由混沌信号驱动的线性自回归（AR）和滑动平均（MA）系统。此外，MNPE方法是非常强大的意义上，额外的系统参数估计为零。换句话说，MNPE方法不需要一个单独的阶确定过程，这个混沌系统的识别问题。蒙特卡罗模拟进行验证MNPE方法的效率。结果表明，MNPE方法比基于白色高斯驱动信号和最小二乘估计器的最优参数辨识方法更有效。将该方法应用于混沌键控通信系统中，设计了一种新的接收机，该接收机可以均衡信道效应。
The problem of blind identification of a linear system driven by a discrete-time chaotic signal is considered in this paper. Based on the short-term predictability of a chaotic signal, an efficient inverse filtering identification method called the minimum nonlinear prediction error (MNPE) technique is proposed. The nonlinear prediction error (NPE) criterion is used as the objective function for inverse filtering. It is shown that the proposed MNPE inverse filtering method can identify linear autoregressive (AR) and moving average (MA) systems driven by chaotic signals accurately. In addition, the MNPE method is very robust in the sense that extra system parameters are estimated as zeros. In other words, the MNPE method does not require a separate order determination procedure for this chaotic system identification problem. Monte Carlo simulations are carried out to validate the efficiency of the MNPE method. Results show that the MNPE method is more effective than the optimal statistic-based identification method based on a white Gaussian driven signal and a least-square estimator. The proposed method is applied to design a novel receiver for a chaos shift keying communications system, which can equalize the channel effects.