Causal or Not? A Definite Answer for Frequency-Response Data

被引:0
作者
Lemus, Andria [1 ]
Engin, Arif Ege [1 ]
机构
[1] San Diego State Univ, Dept Elect & Comp Engn, San Diego, CA 92182 USA
来源
2023 IEEE 32ND CONFERENCE ON ELECTRICAL PERFORMANCE OF ELECTRONIC PACKAGING AND SYSTEMS, EPEPS | 2023年
关键词
rational function approximation; blackbox macromodeling; causality; measurement data;
D O I
10.1109/EPEPS58208.2023.10314917
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
This paper poses the question of whether a dataset can be identified to be causal by employing rational function fitting techniques. Specifically, overfitting is considered to be a factor that could prevent the user from correctly identifying causality. Without the emphasis on overfitting, we show that every frequency-response data is causal. The ability of the adaptive generation (AG) scheme in highlighting an ideal model order is used as an example where the fit may inform the user about the causal quality of the data.
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页数:3
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