基于P范数的核最小对数绝对差自适应滤波算法

为了进一步提高在a稳定分布噪声背景下非线性自适应滤波算法的收敛速度,本文提出了一种新的基于p范数的核最小对数绝对差自适应滤波算法(kernel least logarithm absolute difference algorithm based on p-norm, P-KLLAD).该算法结合核最小对数绝对差算法和p范数,一方面利用最小对数绝对差准则保证了算法在a稳定分布噪声环境下良好的鲁棒性,另一方面在误差的绝对值上添加p范数,通过p范数和一个正常数a来控制算法的陡峭程度,从而提高该算法的收敛速度.在非线性系统辨识和Mackey-Glass混沌时间序列预测的仿真结果表明,本文算法在保证鲁棒性能的同时提高了收敛速度,并且在收敛速度和鲁棒性方面优于核最小均方误差算法、核分式低次幂算法、核最小对数绝对差算法和核最小平均p范数算法.

Kernel least logarithm absolute difference algorithm based on P-norm

The kernel adaptive filtering is an efficient and nonlinear approximation method which is developed in reproducing kernel Hilbert space(RKHS).Kernel function is used to map input data from original space to RKHS space,thus solving nonlinear problems is efficient.Impulse noise and non-Gaussian noise exist in the real application environment,and the probability density distribution of these noise characteristics shows a relatively heavy trailing phenomenon in the statistical sense.αstable distribution can be used to model this kind of non-Gaussian noise well.The kernel least mean square(KLMS)algorithms usually perform well in Gaussian noise,but the mean square error criterion only captures the second-order statistics of the error signal,this type of algorithm is very sensitive to outliers,in other words,it lacks robustness inαstable distribution noise.The kernel least logarithm absolute difference(KLLAD)algorithm can deal with outliers well,but it has the problem of slow convergence.In order to further improve the convergence speed of nonlinear adaptive filtering algorithm inαstable distributed noise background,a new kernel least logarithm absolute difference algorithm based on p-norm(P-KLLAD)is presented in this paper.The algorithm combining least logarithm absolute difference algorithm and p norm,on the one hand,the least logarithm difference criteria is ensure the algorithm to have good robustness inαstable distribution noise environment,and on the other hand,add p norm on the absolute value of error.The steepness of the cost function is controlled by p norm and a posititive constantɑto improve the convergence speed of the algorithm.The computer simulation results of Mackey-Glass chaotic time series prediction and nonlinear system identification show that this algorithm improves the convergence speed with good robustness,and the convergence speed and robustness better than the kernel least mean square algorithm,the kernel fractional lower power algorithm,the kernel least logarithm absolute difference algorithm and the kernel least mean p-norm algorithm.