A stochastic gradient adaptive filter with gradient adaptive step size
A stochastic gradient adaptive filter with gradient adaptive step size
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DOI：
10.1109/78.218137
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发表时间：
1993-06
期刊：
影响因子：
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通讯作者：
V. J. Mathews;Zhenhua Xie
中科院分区：
文献类型：
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作者：
V. J. Mathews;Zhenhua Xie
该自适应滤波器的步长根据梯度下降算法来改变，该梯度下降算法被设计成在每次迭代期间减少平方估计误差。当输入信号为零均值、白色噪声和高斯噪声，且最优系数集为时变随机游走模型时，对自适应滤波器的性能进行了近似分析。该算法具有很好的收敛速度和较低的稳态失调。这些算法在非平稳环境中的跟踪性能对自适应滤波器的参数的选择相对不敏感，并且对于自适应算法的步长的大范围值，非常接近最小均方（LMS）算法的最佳可能性能。仿真实例表明了自适应滤波器的良好性能，并验证了分析结果。>
The step size of this adaptive filter is changed according to a gradient descent algorithm designed to reduce the squared estimation error during each iteration. An approximate analysis of the performance of the adaptive filter when its inputs are zero mean, white, and Gaussian noise and the set of optimal coefficients are time varying according to a random-walk model is presented. The algorithm has very good convergence speed and low steady-state misadjustment. The tracking performance of these algorithms in nonstationary environments is relatively insensitive to the choice of the parameters of the adaptive filter and is very close to the best possible performance of the least mean square (LMS) algorithm for a large range of values of the step size of the adaptation algorithm. Several simulation examples demonstrating the good properties of the adaptive filters as well as verifying the analytical results are also presented. >