摘要
面向结构健康监测,对神经网络的工作性能从概率角度给予细致分析,区分了网络工作的两类典型错误。发现了影响网络工作性能的3个参数,并且指出随着时间的推移,3个参数对网络工作性能有着不同的影响。基于上述分析提出了面向长期结构健康监测的神经网络"错误抑制策略",可以根据结构健康监测的要求来灵活抑制两类错误。最后将该策略应用于BP网络设计,重新定义了网络误差能量函数,给出了错误抑制系数的建议公式,推导了网络学习的权值修正公式。
The performance of neural networks applied in structural health monitoring（SHM） is carefully discussed by using probability tool. Then two types of errors of the network are distinguished. And the further study shows that there are 3 parameters that can influence the performance of the network and the influence varies with the structural serving time. A SHM-oriented mistake curbing strategy is presented to agilely curb the two types of errors on demand of SHM. Then it is applied in designing Back-Propagation neural network. The new error energy function is defined and the way setting the value of mistake curbing coefficient is suggested in the consideration of long time monitoring. Further more, the weight updating rule is derived.
出处
《工程力学》
EI
CSCD
北大核心
2008年第7期74-78,共5页
Engineering Mechanics
基金
国家自然科学基金(50378017)
国家863基金(2006AA04Z416)
南航青年科研基金(Y0513-013)
关键词
健康监测
损伤识别
神经网络
性能分析
错误抑制
BP算法
health monitoring
damage detection
neural networks
performance analysis
mistake curbing
BP algorithm
