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基于改进多输出支持向量机的声发射源定位研究
Alternative TitleResearch on acoustic emission source localization based on improved multi-output SVM
朱军; 史勃; 张环宇; 荣胜波; 黄益泽
Department数字工厂研究室
Source Publication传感器与微系统
ISSN2096-2436
2018
Volume37Issue:2Pages:64-66, 71
Indexed ByCSCD
CSCD IDCSCD:6160143
Contribution Rank1
Funding Organization沈阳市科技局科技重大攻关(创新专项)基金资助项目(F15-007-2-00)
Keyword声发射 核主成分分析 多输出支持向量机 参数优化
Abstract为了提高根据声发射(AE)现象预报煤与瓦斯突出位置的精度,结合核主成分分析(KPCA),提出了一种改进的多输出最小二乘支持向量机(LSSVM)的目标定位方法。对于采集的声发射参数信号,采用核主成分分析提取重要定位特征;采用多输出最小二乘支持向量机建立定位模型,采用文化基因算法对多输出最小二乘支持向量机参数进行优化。试验测试定位性能,结果表明:算法提高了试验平台声发射定位的精度且定位时间少于其他定位算法,具有很高的实际应用价值。
Other AbstractIn order to improve precision of predicting position of coal and gas outburst based on acoustic emission ( AE) phenomena, combined with kernel principal component analysis( KPCA) ,a target positioning method based on improved multi-output least squares ( LS) support vector machine ( SVM) ,KPCA-LSSVM, is proposed. For collected acoustic emission parameter signals,KPCA is used to extract important localization features. Multi-output least squares support vector machine is used to establish positioning model. Memetic algorithm is used to optimize the parameters of the multi-output LSSVM. Simulation experiment is used to test positioning performance. The results show that the algorithm improves precision of acoustic emission localization of test platform and positioning time is less than other positioning algorithms, it has high practical application value.
Language中文
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/21608
Collection数字工厂研究室
Corresponding Author朱军
Affiliation中国科学院沈阳自动化研究所
Recommended Citation
GB/T 7714
朱军,史勃,张环宇,等. 基于改进多输出支持向量机的声发射源定位研究[J]. 传感器与微系统,2018,37(2):64-66, 71.
APA 朱军,史勃,张环宇,荣胜波,&黄益泽.(2018).基于改进多输出支持向量机的声发射源定位研究.传感器与微系统,37(2),64-66, 71.
MLA 朱军,et al."基于改进多输出支持向量机的声发射源定位研究".传感器与微系统 37.2(2018):64-66, 71.
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