Fault Diagnosis of Analog IC Based on Wavelet Neural Network Ensemble

被引：0

作者：

Zuo, Lei ^{[1
]}

Hou, Ligang ^{[1
]}

Wu, Wuchen ^{[1
]}

Wang, Jinhui ^{[1
]}

Geng, Shuqin ^{[1
]}

机构：

[1] Beijing Univ Technol, VLSI & Syst Lab, Beijing 100022, Peoples R China

来源：

ADVANCES IN NEURAL NETWORKS - ISNN 2009, PT 3, PROCEEDINGS | 2009年 / 5553卷

关键词：

Wavelet neural network ensemble; Fault diagnosis; Adaboost;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A new method of analog IC fault diagnosis is proposed in this paper, which is based on wavelet neural network ensemble (WNNE) technique and Adaboost algorithm. This makes the way of the directory be of use in fault, and enhances the validity of the fault diagnosis. Using wavelet decomposition as a tool for extracting feature, Then, after training the WNNE by faulty feature vectors, the fault diagnosis of a radar scanning circuit is implemented with this new. method. The simulation results show that the new method is more effective than the traditional wavelet neural network (WNN) method.

引用

页码：772 / 779

页数：8

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