摘要
在现代桥梁多传感器健康监测系统基础上,建立了基于BP神经网络的桥梁多传感器信息融合系统。首先对桥梁不同断面的倾角传感器采集的样本数据进行特征提取,并将特征值进行关联组成特征序列,利用BP神经网络在模式识别问题上的优势,对特征序列进行信息特征层融合。通过仿真实验,设计了可用于一般桥梁数据模式识别问题的3层BP神经网络,且具有准确率高、收敛快的特点。
On the basis of modern bridge health monitoring system for many sensors,bridge multisensor information fusion system based on BP Neural Network was established.Through the obliquity sensors to collect sample data in feature extraction of bridges of different section,and characteristic sequence was associated using characteristic.Advantages of the BP neural network in the field of pattern recognition were used,and signature sequence was fused on the layer feature.Through the simulation experiment,a three-layer BP neural network that can be generally used in the pattern recognition problem of bridge data was designed,which has characteristics of high accuracy and fast convergence.
出处
《仪表技术与传感器》
CSCD
北大核心
2017年第3期110-112,117,共4页
Instrument Technique and Sensor
关键词
信息融合
BP神经网络
模式识别
特征层融合
information fusion
BP neural network
pattern recognition
fusion on feature layer
