摘要
核最小均方(KLMS)算法在非线性系统中收敛性能较好,但其使用瞬时梯度估计均方误差梯度,导致随机性较大。而块自适应滤波理论利用多个输入-输出的误差来估计均方误差梯度,可降低KLMS算法稳态误差。为此,将块自适应滤波理论运用到KLMS算法中,提出核块最小均方(KBLMS)算法,根据最陡下降法原理推导出KBLM S权矢量更新公式,使用核方法计算得到滤波器输出表达式,并通过并行处理减小算法计算复杂度。仿真结果表明,KBLMS算法可有效提高KLMS算法的稳态性能,并且相比块最小均方算法具有更低的误码率。
Kernel Least Mean Square（ KLMS） algorithm has a good covergence performance in nonlinear systems. But its mean square error gradient is estimated by the instantaneous gradient that results in larger randomness. However,the block adaptive filtering theory can reduce the steady-state error of KLMS algorithm by estimating the mean square error gradient with the multiple input-output errors. For this purpose,the block adaptive filtering theory is applied to the KLMS algorithm, and the Kernel Block Least Mean Square（ KBLMS） algorithm is proposed. Based on the basic idea of the steepest descent algorithm,the weight vector update equation of KBLMS is derived. Then the filter output expression is calculated by utilizing the kernel method,and the computational complexity is reduced by using parallel processing.Simulations results show that KBLMS effectively improves the steady-state performance of KLMS and has lower Bit Error Rate（ BER） than Block Least mean Square（ BLMS） algorithin.
出处
《计算机工程》
CAS
CSCD
北大核心
2017年第9期162-166,共5页
Computer Engineering
关键词
核最小均方算法
块自适应滤波
最陡下降法
核方法
非线性信道均衡
Kernael Least Mean Square（KLMS） algorithm
block adaptive filtering
steepest descent algorithm
kernel method
nonlinear channel equalization
