【摘要】 文中提出了一种基于分数低阶统计误差准则的抗非高斯冲激噪声的核分式低次幂(KFLP)算法。在存在脉冲干扰的环境下,该算法利用权重更新公式中存在瞬时估计误差的倒数系数的有利特性,使得算法在瞬时估计误差突然增大时的权重向量自动停止更新,由此消除了脉冲干扰对权重向量的影响。仿真结果表明,在相同的冲激噪声环境下,随着代价函数的幂次逐渐趋近于1,核分式低次幂算法的稳定性将得到进一步的提高。另一方面,在非高斯脉冲环境下与采用传统的均方误差准则的核最小均方(Kernel Least-Mean-Square,KLMS)算法相比,所提算法的收敛曲线更加平滑,性能更加稳定。更多还原

【Abstract】 To filter out the non-Gaussian impulsive noises,a kernel fractional lower power(KFLP) algorithm based on the fractional lower order statistics error criterion was proposed.Due to the favorable characteristics of the fractional lower order power coefficient of reciprocal,the adaptive update of the weight vector will stop automatically in the pre-sence of impulsive interference.Thus,the effect of updating the weight vector caused by the impulse interference is eliminated.Simulation results show that as the power of the cost function approaches unity,the robustness of the kernel-type low-power algorithm improves in the non-Gaussian impulsive environment.Moreover,compared with the kernel least-mean-square(KLMS) algorithm based on the mean square error criterion,the proposed algorithm has smoother convergence curve and more stable performance.更多还原