• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International Journal of Sustainable Materials and Structural Systems >Structural damage detection using artificial neural networks and least square support vector machine with particle swarm harmony search algorithm
【24h】

Structural damage detection using artificial neural networks and least square support vector machine with particle swarm harmony search algorithm

机译：人工神经网络和最小二乘支持向量机结合粒子群和声搜索算法进行结构损伤检测

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The study presented herein compares the performance of structural damage detection using artificial neural networks (ANNs) and least square support vector machines (LS-SMVs). Structural response signals under ambient vibration are processed according to wavelet energy spectrum for feature extraction. The feature vectors are used as inputs to both classifiers based on ANNs and LS-SVMs. LS-SVM parameters along with the selection of input features are optimised using particle swarm harmony search (PSHS) algorithm with a distance evaluation fitness function. The PSHS that has been introduced in this paper is a new hybrid meta-heuristic algorithm for improving the accuracy and the convergence rate of harmony search (HS) algorithm. The effectiveness of different feature extraction methods and different optimisation algorithms are investigated. This investigation shows that although performance of both classifiers is improved by employing PSHS-based selection, for most cases considered, the classification accuracy of LS-SVM is better than ANN. Furthermore, the results demonstrate the efficiency and the robustness of PSHS.
机译：本文介绍的研究比较了使用人工神经网络（ANN）和最小二乘支持向量机（LS-SMV）进行结构损伤检测的性能。根据小波能谱对环境振动下的结构响应信号进行处理，以提取特征。特征向量用作基于ANN和LS-SVM的两个分类器的输入。 LS-SVM参数以及输入特征的选择使用具有距离评估适应度函数的粒子群和声搜索（PSHS）算法进行了优化。本文介绍的PSHS是一种新的混合元启发式算法，用于提高和声搜索（HS）算法的准确性和收敛速度。研究了不同特征提取方法和不同优化算法的有效性。这项研究表明，尽管通过使用基于PSHS的选择可以提高两个分类器的性能，但在大多数情况下，LS-SVM的分类精度都优于ANN。此外，结果证明了PSHS的效率和鲁棒性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号