基于Weiner模型超混沌lü系统的自适应辨识*

摘要

为了能实时而有效地辨识参数不确定的超混沌lü系统，以便于对该系统进行控制或跟踪，本文提出了一种基于Wiener模型自适应分段线性(PWL)滤波器的超混沌系统辨识方法。 Wiener模型的线性部分采用了线性横向滤波器，非线性部分用分段线性滤波器近似表示。根据最小均方误差准则导出了滤波器参数更新算法，并进一步推导出算法的收敛性条件。计算机仿真证实了该自适应滤波器辨识超混沌系统的有效性。该方法不仅克服了自适应线性滤波器难以辨识出这类强非线性系统，而且比其他非线性自适应滤波器的计算复杂性低得多。%In order to be able to identify the hyper-chaotic lü system with uncertain parameters effectively in real time, so that hyper-chaotic system control and synchronization tracking can be applied, this paper presents a system identification method for the hyper-chaotic system based on Wiener model. The linear part of the Wiener model consists of linear transversal filters, while the nonlinear part is represented approximately by piecewise linear filters. According to the minimum mean square error criterion, the filter parameter updated algorithm is derived, and the convergence condition is also obtained. Simulation results confirm the effectiveness of the adaptive filter for the identification of hyper-chaotic systems. The presented method not only overcomes the difficulty to identify a strongly nonlinear system only by adaptive linear filters, but also have a lower computational complexity compared with other non-linear adaptive filters.