Joint Learning of Model Parameters and Coefficients for Online Nonlinear Estimation
Joint Learning of Model Parameters and Coefficients for Online Nonlinear Estimation
复制标题
DOI：
10.1109/access.2021.3053651
复制
发表时间：
2021
期刊：
影响因子：
3.9
通讯作者：
Masa-aki Takizawa;M. Yukawa
中科院分区：
文献类型：
--
作者：
Masa-aki Takizawa;M. Yukawa
我们提出了一种新的在线有效的非线性估计算法。将目标非线性函数的参数作为变量的一部分，用“不固定”的高斯函数进行逼近。更新高斯参数(尺度和中心)以及系数，以抑制由系数的$ell1}$范数规则化的瞬时平方误差，以提高模型的效率。提高模型效率的另一点是多尺度筛选方法，这是一种分层的字典增长方案，用于初始化具有多个选择的高斯尺度。为了降低计算复杂度，提出了一种扩展字典和更新高斯参数的选择策略。计算机实验表明，该算法具有很强的自适应能力，并能产生有效的估计。
We propose a novel online algorithm for efficient nonlinear estimation. Target nonlinear functions are approximated with “unfixed” Gaussians of which the parameters are regarded as (a part of) variables. The Gaussian parameters (scales and centers), as well as the coefficients, are updated to suppress the instantaneous squared errors regularized by the $\ell _{1}$ norm of the coefficients to enhance the model efficiency. Another point for enhancing the model efficiency is the multiscale screening method, which is a hierarchical dictionary growing scheme to initialize Gaussian scales with multiple choices. To reduce the computational complexity, a certain selection strategy is presented for growing the dictionary and updating the Gaussian parameters. Computer experiments show that the proposed algorithm enjoys high adaptation-capability and produces efficient estimates.