Structural damage localization using probabilistic neural networks

摘要：

In this paper, the structural damage localization on a simple composite plate specimen is identified using probabilistic neural networks. First, the category to be identified is defined according to the structural location, and the number of categories is reduced by grouping neighboring elements to one category. Second, the state data of damaged structure are collected by a data collection system, and are utilized as feature vectors for the probabilistic neural network. Finally, the smoothing parameter in the probabilistic neural network is studied. When this trained network is subjected to the measured response, it should be able to locate existing damage. The effectiveness of the proposed method is demonstrated.

关键词： Damage localization Probabilistic neural networks Smoothing parameter

作者: Peng Li

作者单位: School of Mechanical and Electronical Engineering, East China Jiaotong University, Nanchang, 330013, PR China

会议类型: 国际会议

会议名称: The 4th IFIP International on Computer and Computing Technologies in Agriculture and the 4th Symposium on Development of Rural Information(第四届国际计算机及计算机技术在农业中的应用研讨会暨第四届中国农业信息化发展论坛 CCTA 2010)

会议地点: 南昌

会议语种:英文

页码: 965-969

在线出版日期: 2010-10-22（万方平台首次上网日期，不代表论文的发表时间）