Neural network have the abilities of self-learning, adapt and parallel management, but the structural selection lacks academic bases.
神经网络具有自学习，自适应，并行处理的能力，但结构的选择缺乏理论依据。
Based on this, bring forward the BP neural network structural damage detection method based on modal parameters.
在此基础上提出了基于模态参数的BP神经网络结构损伤识别方法。
Due to its structural simplicity, the radial basis function (RBF) neural network has been widely used for approximation and classification.
径向基函数(RBF)神经网络因其结构简单而被广泛地用于非线性函数近似和数据分类。
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.
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。
Applying neural network to predict structural response may solve the problem of time lag in active control and offer basis for controlling decision.
应用神经网络预测结构响应可以解决主动控制中的时滞问题，为控制决策提供依据。
The whole protein sequence and a neural network were used to predict protein structural classes.
利用神经网络和蛋白质全序列对蛋白质结构类进行了预测。
The exponential stability and trajectory bounds of the motions of equilibria of an associative neural network under structural variations while learning a new pattern are investigated.
本文讨论了一类联想神经网络在学习过程中结构变化引起网络平衡点状态变化的动态特性；研究了网络的指数稳定性质；
Artificial neural network is one of artificial system, which simulates structural and functional characteristics of biological neural network resorted to engineering technological means.
而神经网络则是用工程技术手段模拟生物神经网络的结构特征和功能特征的一类人工系统。
Secondly, according to the change of damage structural frequency and modal, the fixity factor is identified by RBF neural network, then FEM correction of truss is finished.
其次，根据损伤后的结构频率与模态的变化，应用径向基神经网络，进行结构节点固结系数的识别，从而实现对网架结构有限元模型的修正。
An updating technique for structural dynamic models is proposed in this study on the basis of neural network.
提出了一种基于神经网络的结构动力模型修正方法。
The system consists of graphic user's interface module, sample processing module, neural network structural module and neural network computing module.
该系统主要由以下模块构成：用户界面模块、网络模型模块、样本处理模块、神经网络计算模块以及神经网络训练结果显示模块。
Based on the theorem of the existence of multilayered neural network mapping, a model of artificial neural network is set up for approximate structural analysis.
基于多层神经网络映射存在定理，建立近似结构分析的人工神经网络模型。
Therefore, through structural modal analysis with different damage degree using ANSYS, get natural frequencies and mode shape data as neural network input vector after unitary.
因此，通过有限元软件ANSYS对具有各种损伤程度的结构进行模态分析，得到固有频率和模态分量的数据，经过归一化处理后作为神经网络的输入向量。
The results show that while the gray system can be very successful in structural damage prediction, neural network technique is applicable to irregular structural damage detection.
结果表明，灰色理论能成功地对结构损伤进行预测，神经网络适用于此类损伤无规律对象问题的诊断。
The basic structure of evolving knowledge library, the structural principle of RBF network and the procedures of building knowledge library with RBF neural network are presented.
介绍了产生式知识库的基本结构，RBF网络的构成原理及采用RBF神经网络构建知识库的过程。
Using MATLAB6.5 BP neural network toolbox, compiles the structural damage detection process with the input parameters based on the modal parameters of frequency and mode shape.
以MATLAB6.5BP神经网络工具箱为基础，编写了基于频率平方变化和振型模态分量为输入参数的BP神经网络结构损伤识别程序。
Use them for structural damage signals characteristic extraction, and signal extraction as neural network input, the introduction of three-tier network model BP damage to structures identification.
利用它们对结构损伤信号进行特征提取，并将提取的信号作为神经网络的输入，采用三层BP网络模型对结构物的损伤进行识别。
In order to solve the problems of feature extraction and calibration modeling in the area of quantitatively infrared spectral analysis, a structural adaptive neural network is proposed.
为了解决红外光谱定量分析中的特征提取和校正规模问题，提出了一种输入层自构造神经网络。
The validity of structural damage detection using neural network strongly depends on the training sample.
用神经网络进行结构损伤检测、分析的有效性在很大程度上取决于训练样本的好坏。
The validity of structural damage detection using neural network strongly depends on the training sample.
用神经网络进行结构损伤检测、分析的有效性在很大程度上取决于训练样本的好坏。
应用推荐