Model Order Reduction of Nonlinear Circuit using Proper Orthogonal Decomposition and Nonlinear Autoregressive with eXogenous input (NARX) Neural Network

被引：0
|
作者：
Nagaraj, S. [1 ]
Seshachalam, D. [1 ]
Hucharaddi, Sunil [1 ]
机构：
[1] BMS Coll Engn, Dept E&C, Bengaluru, India
来源：
PROCEEDINGS OF THE 2018 16TH ACM/IEEE INTERNATIONAL CONFERENCE ON FORMAL METHODS AND MODELS FOR SYSTEM DESIGN (MEMOCODE) | 2018年
关键词：
Model order reduction; non linear modeling; proper orthogonal decomposition; singular value decomposition; NARX neural network; OPERATION;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
In this paper, we present a novel model order reduction (MOR) technique based on the proper orthogonal decomposition (POD) and Nonlinear Autoregressive with eXogenous inputs (NARX) neural network. In this proposed method, a set of solutions, referred as snapshots of the original problem are obtained at relatively large intervals. Proper orthogonal decomposition (POD) is used to reduce the order of the nonlinear model using the snapshot. The reduced system is modeled using NARX neural network. On applying the method to nonlinear electrical circuit, the results obtained, verify the validity of the new MOR technique.
引用
收藏
页码：47 / 50
页数：4
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