Steepening Squared Error Function Facilitates Online Adaptation of Gaussian Scales
Steepening Squared Error Function Facilitates Online Adaptation of Gaussian Scales
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DOI：
10.1109/icassp40776.2020.9054092
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发表时间：
2020-05
期刊：
影响因子：
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通讯作者：
Masa-aki Takizawa;M. Yukawa
中科院分区：
文献类型：
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作者：
Masa-aki Takizawa;M. Yukawa
我们之前提出了一种用于在线非线性估计的高斯参数（尺度和中心）和系数的联合学习方案。然而，以高斯尺度表示的瞬时平方误差代价，当初始猜测远不是最优时，往往具有较浅的斜率，导致极其缓慢的收敛。在本文中，我们提出通过添加一个距离的平方函数从即时最优尺度陡增成本函数。数值算例表明，陡化代价的使用改善了尺度参数在不适当初始尺度设置下的收敛行为。
We previously proposed a joint learning scheme of Gaussian parameters (scales and centers) and coefficients for online nonlinear estimation. The instantaneous squared error cost in terms of the Gaussian scales, however, tends to have shallow slopes when the initial guess is far from optimal, causing extremely slow convergence. In this paper, we propose steepening the cost function by adding a squared distance function from the instantaneously-optimal scale. Numerical examples show that the use of the steepened cost ameliorates the convergence behaviors of the scale parameters in inappropriate initial-scale settings.