Deep kernel recursive least-squares algorithm

被引：3
|
作者：
Mohamadipanah, Hossein [1 ]
Heydari, Mahdi
Chowdhary, Girish [2 ]
机构：
[1] Stanford Univ, Stanford, CA 94305 USA
[2] Univ Illinois, Urbana, IL 61801 USA
来源：
NONLINEAR DYNAMICS | 2021年 / 104卷 / 03期
关键词：
Kernel recursive least square; Deep hierarchical structure; Multidimensional dataset; MEAN-SQUARE; DAMAGE DETECTION; TRANSMISSIBILITY;
D O I：
10.1007/s11071-021-06416-0
中图分类号：
TH [机械、仪表工业];
学科分类号：
0802 ;
摘要：
We present a new kernel-based algorithm for modeling evenly distributed multidimensional datasets that does not rely on input space sparsification. The presented method reorganizes the typical single-layer kernel-based model into a deep hierarchical structure, such that the weights of a kernel model over each dimension are modeled over its adjacent dimension. We show that modeling weights in the suggested structure leads to significant computational speedup and improved modeling accuracy.
引用
收藏
页码：2515 / 2530
页数：16
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