Feature Extraction of Waveform Signals for Uncertain Dynamic Processes Using Neural Networks

Yaw-Jen Chang, C. Chang, Jui-Ju Tsai
{"title":"Feature Extraction of Waveform Signals for Uncertain Dynamic Processes Using Neural Networks","authors":"Yaw-Jen Chang, C. Chang, Jui-Ju Tsai","doi":"10.1109/IJCNN.2007.4371338","DOIUrl":null,"url":null,"abstract":"This paper presents a novel and simple feature extraction approach for drawing out the signal characteristics of uncertain dynamic processes by the feature neurons. Kohonen network is used to construct the feature neurons to represent its respective local features of a waveform signal. For a class of waveform signals, groups of feature neurons can be obtained. Incorporating with the ellipsoidal calculus, this approach can extract the process drifts and abnormal deviations in the process characteristics by limit checking. Moreover, it is robust even for the process with different process time durations. For the system with oscillatory transient response, this approach can be iteratively used to augment the amount of feature neurons to analyze the characteristics of any portion of the signal of interest in detail. With the merit of unsophisticatedness, this approach can be implemented for the determination of preventive maintenance and fault detection in the semiconductor manufacturing.","PeriodicalId":350091,"journal":{"name":"2007 International Joint Conference on Neural Networks","volume":"4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 International Joint Conference on Neural Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IJCNN.2007.4371338","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

This paper presents a novel and simple feature extraction approach for drawing out the signal characteristics of uncertain dynamic processes by the feature neurons. Kohonen network is used to construct the feature neurons to represent its respective local features of a waveform signal. For a class of waveform signals, groups of feature neurons can be obtained. Incorporating with the ellipsoidal calculus, this approach can extract the process drifts and abnormal deviations in the process characteristics by limit checking. Moreover, it is robust even for the process with different process time durations. For the system with oscillatory transient response, this approach can be iteratively used to augment the amount of feature neurons to analyze the characteristics of any portion of the signal of interest in detail. With the merit of unsophisticatedness, this approach can be implemented for the determination of preventive maintenance and fault detection in the semiconductor manufacturing.
基于神经网络的不确定动态过程波形信号特征提取
本文提出了一种新颖、简单的特征提取方法,利用特征神经元提取不确定动态过程的信号特征。利用Kohonen网络构造特征神经元来表示波形信号各自的局部特征。对于一类波形信号,可以得到一组特征神经元。该方法结合椭球微积分法,通过极限校核提取过程漂移和过程特性中的异常偏差。此外,即使对于具有不同过程时间持续时间的过程,它也具有鲁棒性。对于具有振荡瞬态响应的系统,这种方法可以迭代地用于增加特征神经元的数量,以详细分析感兴趣的信号的任何部分的特征。该方法具有简单易行的优点,可用于半导体制造中的预防性维护和故障检测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信