摘要
为了改善固定遗忘因子递推最小二乘(RLS)算法在时变系统中的跟踪性能,提出一种改进的RLS算法。改进的可变遗忘因子RLS算法,不仅克服了固定遗忘因子RLS算法中跟踪速度和参数失调的矛盾,而且避免了当参数估值趋于参数真值时,卡尔曼增益趋于零,RLS算法失去对时变系统的跟踪能力的问题。最后,在MATLAB仿真平台下,对改进的RLS算法性能进行仿真验证。仿真结果表明,改进的算法能够获得快速的跟踪能力,也具有较快的收敛速度和较小的稳态误差。
In order to improve the tracking performance of the fixed forgetting factor RLS algorithm in the time-varying system,a modified RLS algorithm is proposed.The modified variable forgetting factor RLS overcomes the contravention between the tracking velocity and parameters misad-justment in the fixed forgetting factor RLS algorithm.What’s more,Kalman gain will tend to zero as the parameteresti-mates approach their true values.As a result,RLS algorithm will eventually lose its tracking ability in the time-varying system.The proposed algorithm can solve this problem.In the end,the modified RLS algorithm is simulated in the MATLAB simulation platform.The simulation results prove that this algorithm has high tracking velocity as well as high convergence velocity and small stationary error.
出处
《计算机工程与应用》
CSCD
北大核心
2011年第23期147-149,227,共4页
Computer Engineering and Applications
关键词
自适应滤波
递推最小二乘算法
可变遗忘因子
双曲正切函数
adaptive filtering
Recursive-Least-Square（RLS）algorithm
variable forgetting factor
hyperbolic tangent function
