基于极限学习机的结构健康监测数据恢复

摘要

为解决桥梁结构健康监测系统中数据丢失问题,引入格兰杰因果关系分析各传感器变量数据间的关系,选择与传感器丢失数据格兰杰因果关系大的变量作为极限学习机的输入向量,实现丢失数据的恢复.通过实际桥梁监测丢失数据的仿真实验,以均方根误差和最大误差绝对值作为评估指标,并与反向传播网络和最小二乘支持向量机算法对比,结果表明该方法在理论和实践上是正确和可行的.%For the problem of data lose in structural healthy monitoring system for the bridge, Granger causality test is introduced to calculate the casual relation between two sensors, and select the sensor signal of larger relation as input vector for extreme learning machine to recover the lost sensor signal data. The proposed data recover algorithm is tested in the case of the bridge structural health monitoring system, and the simulation results indicate the algorithm is correct and efficient in theory and practice with the performance index for mean square error and largest error absolute value, compared with Back Propagation(BP) network and Least Squares Support Vector Machine(LS_SVM).