摘要:通过对归一化LMS算法(NLMS)进行拉格朗日方程最小值运算而得到的CS-LMS算法具有良好的收敛性和灵活性。该文为进一步解决自适应滤波算法不能有效处理既要求收敛速度快又要求稳态误差小的矛盾,建立了步长与信号误差之间的一种非线性函数模型,并将改进的CS-LMS算法应用到混沌通信中。仿真结果表明,改进算法的收敛速度和稳态误差性能都有较大的提高。
关键词:LMS;步长;非线性函数模型;自适应CS-LMS算法;混沌通信
中图分类号:TP311文献标识码:A文章编号:1009-3044(2012)16-3867-02
An Improved Adaptive CS-LMS Algorithm and its Performance Analysis in Chaotic Communication
SONG Guo-dong, ZHANG Huai-yuan
(Electronic and Information Engineering College of Southwest University , Chongqing 400715,China)
Abstract: CS-LMS algorithm provides faster convergence and higher flexibility than the normalized least mean square (NLMS) by mini? mizing the Lagrangian function. This paper, in order to solve adaptive filter’s conflict of gaining the fast convergence speed and low steady state error, a non- linear functional model between step size and signal error will be established, and we will continue to apply this new al? gorithm to chaotic communication. Simulation results present the proposed algorithm enhances the speed of convergence and quality of sta? bility distinctly.
Key words: LMS; step size; a non- Linear functional model; adaptive CS-LMS filter; chaotic communication
该文给出的改进的CS-LMS算法,是在步长参数μ与误差信号e(n)之间建立了一种新的非线性函数关系,该算法与传统LMS和CS-LMS算法自适应滤波算法相比,具有较快的收敛速率并且在高信噪比下稳态误差方面表现的比较优越,但在仿真中也发现,非线性函数中的参数对算法性能有着决定性的影响,所以一定要结合实际情况合理选择各类步长参数。
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