摘要
针对非高斯环境下一般自适应滤波算法性能严重下降问题,本文提出了一种基于Softplus函数的核分式低次幂自适应滤波算法(kernel fractional lower algorithm based on Softplus function,SP-KFLP),该算法将Softplus函数与核分式低次幂准则相结合,利用输出误差的非线性饱和特性通过随机梯度下降法更新权重.一方面利用Softplus函数的特点在保证了SP-KFLP算法具有良好的抗脉冲干扰性能的同时提高了其收敛速度;另一方面将低次幂误差的倒数作为权重向量更新公式的系数,利用误差突增使得权重向量不更新的方法来抵制冲激噪声,并对其均方收敛性进行了分析.在系统辨识环境下的仿真表明,该算法很好地兼顾了收敛速度和跟踪性能稳定误差的矛盾,在收敛速度和抗脉冲干扰鲁棒性方面优于核最小均方误差算法、核分式低次幂算法和S型核分式低次幂自适应滤波算法.
Kernel adaptive filters are a class of powerful nonlinear filter developed in reproducing kernel Hilbert space(RKHS).The Gaussian kernel is usually the default kernel in KAF algorithm,because the Gaussian kernel has the universal approximation.However,in previous research the kernel adaptive filtering algorithms were mostly based on mean square error criterion and assumed to be in a Gaussian noise environment.When environmental noise is changed,the performance of conventional kernel adaptive filtering algorithm based on mean square error criterion is seriously reduced to failure due to the interference of non-Gaussian noise and the influence of inappropriate non-Gaussian modeling.Therefore,it is important to develop a new method of suppressing the noise of non-Gaussian signals.In this paper,a new kernel fractional lower power adaptive filtering algorithm is proposed by combining the benefits of the kernel method and a new loss function which is robust against nonGaussian impulsive interferences and has fast convergence under a similar stability condition.The proposed SPKFLP algorithm generates a new framework of cost function which combines the Softplus function with the KFLP algorithm by updating its weight vector according to the gradient estimation while nonlinear saturation characteristics of output error are used.Compared with the features of sigmoid function the features of the Softplus function guarantee the SP-KFLP an excellent performance for combatting impulsive interference and speeding up the convergence rate.In the kernel fractional low power criterion the reciprocal of the system error is used as the coefficient of the weight vector update formula,and the method of error burst is used to make the weight vector not update to resist the impulse noise.The mean square convergence analysis for SP-KFLP is conducted,and a sufficient condition for guaranteeing convergence is therefore obtained by using the energy conservation relation.The proposed algorithm is very simple computationally.Simulations in a system identification show that the proposed SP-KFLP algorithm outperforms the kernel least-mean-square algorithm,kernel fractional lower power algorithm,and sigmoid kernel fractional lower algorithm in terms of convergence rate and the robustness of against impulsive interference.The proposed algorithm improves not only the capability of resisting impulsive interference,but also the convergence rate.In other words,the contradiction between convergence and tracking performance stability is well taken into account,and the performance under Gaussian noise is also better than the performance of the traditional kernel adaptive algorithm.
作者
火元莲
王丹凤
龙小强
连培君
齐永锋
Huo Yuan-Lian;Wang Dan-Feng;Long Xiao-Qiang;Lian Pei-Jun;Qi Yong-Feng(College of Physics and Electronic Engineering,Northwest Normal University,Lanzhou 730000,China;College of Computer Science and Engineering,Northwest Normal University,Lanzhou 730000,China)
出处
《物理学报》
SCIE
EI
CAS
CSCD
北大核心
2021年第2期409-415,共7页
Acta Physica Sinica
基金
国家自然科学基金(批准号:61561044)资助的课题.
