A generalized normalized gradient descent algorithm
A generalized normalized gradient descent algorithm
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DOI：
10.1109/lsp.2003.821649
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发表时间：
2004-02-01
影响因子：
3.9
通讯作者：
Mandic, DP
中科院分区：
文献类型：
--
作者：
Mandic, DP
提出了一种线性有限冲激响应（FIR）自适应滤波器的广义归一化梯度下降（CTNGD）算法。GNGD表示通过在NLMS的学习率的分母中附加梯度自适应项的归一化最小均方（NLMS）算法的扩展。通过这种方式，GNGD根据输入信号的动态来调整其学习速率，其中附加的自适应项补偿了NLMS推导中的简化。GNGD的性能由NLMS的性能从下面限定，而它在NLMS发散的环境中收敛。GNGD被证明是强大的显着变化的初始值的参数。预测设置中的模拟支持分析。
A generalized normalized gradient descent (CTNGD) algorithm for linear finite-impulse response (FIR) adaptive filters is introduced. The GNGD represents an extension of the normalized least mean square (NLMS) algorithm by means of an additional gradient adaptive term in the denominator of the learning rate of NLMS. This way, GNGD adapts its learning rate according to the dynamics of the input signal, with the additional adaptive term compensating for the simplifications in the derivation of NLMS. The performance of GNGD is bounded from below by the performance of the NLMS, whereas it converges in environments where NLMS diverges. The GNGD is shown to be robust to significant variations of initial values of its parameters. Simulations in the prediction setting support the analysis.