1) reweighted interacting multiple models (RIMM) .2) reweighted interacting multiple models kalman filtering(RIMMKF) .
首页词典1) iteratively reweighted least squares.最小二乘迭代法迭代最小二乘法迭代重新加权最小平方法说明:双击或选中下面任意单词,将显示该词的音标、读音、翻译等;选中中文或多个词,将显示翻译。 您的位置:-- 重新加权迭代最小二乘法 首页词典1) iteratively reweighted least squares 重新加权迭代最小二乘法 2) iterative reweighed least squares (IRLS) 迭代重加权最小二乘法 3) iterated weight least square method 迭代
客户端及插件登录/注册Rapid spectral analysis of agro-products using an optimal strategy: dynamic backward interval PLS–competitive adaptive reweighted sampling CAS-3JCR-Q2SCIEEI Xianfeng SongGuotong DuQianqian LiGuo TangYue Huang 难关十问参考文献被引用社区问答发布时间·被引用数·默认排序 发布时间·被引用数·默认排序 我要提问·Computer Science·Variable SelectionDOI: 10.1007/s00216-020-02506-x Analytical and Bioanalytical Chemistry Feb 2020 13被引用 0笔记 PDF引用收藏AI理解论文&经典十问 图表提取 参考文献 被引用 社区问答 领域
In this paper, we present a practical algorithm based on sparsity regularization to effectively solve nonlinear dynamic inverse problems that are encountered in subsurface model calibration. We use an iteratively reweighted algorithm that is widely used to solve linear inverse problems with s.In this paper, we present a practical algorithm based on sparsity regularization to effectively solve nonlinear dynamic inverse problems that are encountered in subsurface model calibration. We use an iteratively reweighted algor
提出了两种多步总变分图像复原算法:多步迭代收缩阈值算法和多步迭代加权收缩算法,并针对多步算法每次迭代需要额外估计权参数的不足,给出了一种固定权参数的多步总变分复原算法。 And,two kinds of multi-step TV restoration algorithms were propo
此算法放宽了直接互相关法对信号与噪声的假设条件且在低信噪比下仍能有效估计时延;在此之时为了提高时延估计精度,在广义相关时延估计的基础上又调查出了基于基小波的二次加权法。 Moreover,a base-wavelet based twice-weighted method was proposed to
本文在经典的Tikhonov正则化方法求解反困难的理论框架下,将传统的总变差正则化方法中的范数进行推广,提出了广义总变差正则化模型,并根据加权迭代最小二乘方法的基本思想,通过加权矩阵将一般的l~p范数转化为l~2范数,由此运用标准的二次优化方法进行迭代求解,这就是本文重点介绍的算法——加权范数迭代算
本文在准解析近似和重加权正则化的共轭梯度法的基础上,用visualFortran6。 We have developed a program for 3D inversion to borehole-surface electrical data based on the quasi-analyt
1) iterative reweighted .To obtain better design results with equiripple error, we apply iterative reweighted techniques to the design of IIR digital filters. .
showing that better likelihood can be achieved with this reweighted wake-sleep procedure. Based on this interpretation, we propose that a sigmoidal belief network is not sufficiently powerful for the layers of the inference network in order to recover a good estimator of the posterior distribution of lat yields substantially better generative models. 全部 机器翻译 AI理解论文&经典十问 难关十问 参考文献被引用社区问答发布时间·被引用数·默认排序 发布时间·被引用数·默认排序 社区问答 我要提问 领域 ·Computer Science