欢迎访问机床与液压官方网站!

咨询热线:020-32385312 32385313 RSS EMAIL-ALERT
基于人工蜂群优化支持向量机的船舶管网泄漏识别
作者:
作者单位:

作者简介:

通讯作者:

中图分类号:

基金项目:

交通运输部科技项目(2012-329-814-220);重庆市自然科学基金项目(cstc2011jjA1056)


Author:
Affiliation:

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    针对船舶管系频繁的调泵、调阀等常规操作极易引发泄漏检测系统误报警这一问题,采用适于小样本数据集处理的支持向量机(SVM)从管系多种工况中识别泄漏。使用具备局部和全局最优解搜索能力的人工蜂群算法(ABC)优化SVM参数,避免陷入局部最优解,提高识别正确率。通过小波分析提取工况特征向量,设计和组建人工蜂群优化支持向量机(ABCSVM)分类器,实现了泄漏识别。与BP神经网络等常用算法比较,ABCSVM算法具有更高的泄漏识别正确率和适应性。

    Abstract:

    Aimed at the problem of routine operations in ship pipeline system of valve and pump adjustment of false alarms of leak detection system were very easy led, by made use of Support Vector Machine (SVM) suitable to process with wavelet data collection, leakage recognition was carried out for the pipeline system in various working conditions. The SVM parameters were optimized by using the Artificial Bee Colony (ABC) with searching power of local and complete area optimal solution, so as to avoid local area optimal solution and improve accuracy rate in recognition. By wavelet analysis to extract eigenvectors in working condition, the classifier of ABCSVM was designed and built up to implement leakage recognition. As compared with the commonly used algorithm of BP neural network, the ABCSVM algorithm has a better accuracy and adaptability in leakage recognition.

    参考文献
    相似文献
    引证文献
引用本文

印洪浩.基于人工蜂群优化支持向量机的船舶管网泄漏识别[J].机床与液压,2014,42(23):75-78.
.[J]. Machine Tool & Hydraulics,2014,42(23):75-78

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:
  • 最后修改日期:
  • 录用日期:
  • 在线发布日期: 2015-06-17
  • 出版日期: