介绍了使用模态指标及神经网络进行结构损伤识别的方法。
The method of damage identification with modal index and the artificial neural networks is introduced in this paper.
鉴于该方法只能识别梁中的单处损伤，提出了结合移动质量法和神经网络进行结构损伤识别的方法。
As the method can only detect single damage in beam, this paper proposes the method of combining moving mass method and neural network to detect structural damage.
基于人工神经网络技术的结构损伤识别方法是大型土木工程结构损伤识别的有效方法，可在工程结构损伤识别中广泛应用。
The method of identification of structural damage based on ANN is an effective measurement to identify the structural damage, which can be widely used in practical engineering.
本文阐述了结构健康监测的内容与要求，以及应用振动诊断方法进行结构损伤识别的技术研究与应用现状。
It elaborates the contents and requirements of the structural health monitoring, and the present research and application of damage identification for structure with vibration diagnosis technique.
从模式识别的观点对结构损伤识别进行了分析，分析了小波包信号能量特征提取的方法。
The structural damage recognition is studied with the mode identification method, and the method for drawing the wavelet package signal energy eigenvalue is analyzed.
从模式识别的观点对结构损伤识别进行了分析，分析了小波包信号能量特征提取的方法。
The structural damage recognition has been analyzed with mode identification method and the method for extracting the wavelet package signal energy eigenvalue has been analyzed.
最后通过一简支试验梁的损伤数值模拟，提出了适用于结构损伤识别的网络训练方法。
In the end, the neural networks training methods, which are proper to apply in the diagnosis the structural damage, are proposed through numerical simulation of damage in a simple experimental beam.
该文在分析了时间序列模型的自回归系数对结构单元刚度灵敏度的基础上，提出了一种采用随机载荷作用下结构的时域响应数据进行损伤识别的新方法。
A new method is developed for identifying structural damages at the element level by using time-domain response data at a few points caused by random loadings.
目的基于自适应共振理论，提出一种基于ART2神经网络的结构损伤识别方法，以实现结构损伤识别的自主学习。
An ART2 neural network based on adaptive resonance theory is put forward in this work to identify the damage of the structures and to realize the on-line self-study of the network.
目的基于自适应共振理论，提出一种基于ART2神经网络的结构损伤识别方法，以实现结构损伤识别的自主学习。
An ART2 neural network based on adaptive resonance theory is put forward in this work to identify the damage of the structures and to realize the on-line self-study of the network.
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