A normalized gradient descent algorithm for nonlinear adaptive filters using a gradient adaptive step size
A normalized gradient descent algorithm for nonlinear adaptive filters using a gradient adaptive step size
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DOI：
10.1109/97.969448
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发表时间：
2001-11
影响因子：
3.9
通讯作者：
Danilo P. Mandic;A. I. Hanna;M. Razaz
中科院分区：
文献类型：
--
作者：
Danilo P. Mandic;A. I. Hanna;M. Razaz
提出了一种用于非线性神经滤波器在线自适应的全自适应归一化非线性梯度下降（FANNGD）算法。通过对输出误差进行泰勒级数展开式的线性化，推导出了使滤波器的瞬时输出误差最小的自适应步长。为严格起见，自适应学习率表达式内截断泰勒级数展开的剩余部分被自适应，并使用梯度下降进行更新。结果表明，FANNGD算法的收敛速度比以前介绍的同类算法快。
A fully adaptive normalized nonlinear gradient descent (FANNGD) algorithm for online adaptation of nonlinear neural filters is proposed. An adaptive stepsize that minimizes the instantaneous output error of the filter is derived using a linearization performed by a Taylor series expansion of the output error. For rigor, the remainder of the truncated Taylor series expansion within the expression for the adaptive learning rate is made adaptive and is updated using gradient descent. The FANNGD algorithm is shown to converge faster than previously introduced algorithms of this kind.