Analog IC Fault Diagnosis based on Wavelet Neural Network Ensemble

被引：1
|
作者：
Zuo Lei [1 ]
Wang Jinhui [1 ]
Hou Ligang [1 ]
Geng Shuqin [1 ]
Wu Wuchen [1 ]
机构：
[1] Beijing Univ Technol, VLSI & Syst Lab, Beijing 100022, Peoples R China
来源：
PROCEEDINGS OF THE 2009 WRI GLOBAL CONGRESS ON INTELLIGENT SYSTEMS, VOL IV | 2009年
关键词：
fault diaghosis; WNNE; Adaboos;
D O I：
10.1109/GCIS.2009.95
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A fault diagnosis method for analog IC diagnosis based on wavelet neural network ensemble (WNNE) and Adaboost algorithm, is proposed in this paper. 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 model of the circuit with fault diagnosis system is built. Simulation results have shown that this claim is more effective than wavelet neural network (WNN).
引用
收藏
页码：45 / 48
页数：4
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