In order to evaluate the failure probability of a complicated structure,the structural responses usually need to be estimated by some numerical analysis methods such as finite element method( FEM). The response surfac...In order to evaluate the failure probability of a complicated structure,the structural responses usually need to be estimated by some numerical analysis methods such as finite element method( FEM). The response surface method( RSM) can be used to reduce the computational effort required for reliability analysis when the performance functions are implicit. However,the conventional RSM is time-consuming or cumbersome if the number of random variables is large. This paper proposes a Legendre orthogonal neural network( LONN)-based RSM to estimate the structural reliability. In this method,the relationship between the random variables and structural responses is established by a LONN model. Then the LONN model is connected to a reliability analysis method,i.e. first-order reliability methods( FORM) to calculate the failure probability of the structure.Numerical examples show that the proposed approach is applicable to structural reliability analysis,as well as the structure with implicit performance functions.展开更多
Aiming at the reliability analysis of small sample data or implicit structural function,a novel structural reliability analysis model based on support vector machine(SVM)and neural network direct integration method(DN...Aiming at the reliability analysis of small sample data or implicit structural function,a novel structural reliability analysis model based on support vector machine(SVM)and neural network direct integration method(DNN)is proposed.Firstly,SVM with good small sample learning ability is used to train small sample data,fit structural performance functions and establish regular integration regions.Secondly,DNN is approximated the integral function to achieve multiple integration in the integration region.Finally,structural reliability was obtained by DNN.Numerical examples are investigated to demonstrate the effectiveness of the present method,which provides a feasible way for the structural reliability analysis.展开更多
依据6 h T213数值预报产品的资料,采用EOF展开和人工神经网络等方法,对卫星云图短时预报方法进行研究。首先对卫星云图灰度值样本序列进行EOF展开,将提取出来的时间系数作为建模的预报量,以数值预报产品的物理量场作为预报因子,建立人...依据6 h T213数值预报产品的资料,采用EOF展开和人工神经网络等方法,对卫星云图短时预报方法进行研究。首先对卫星云图灰度值样本序列进行EOF展开,将提取出来的时间系数作为建模的预报量,以数值预报产品的物理量场作为预报因子,建立人工神经网络预测模型。将预报得到的时间系数与空间特征向量进行时空反演,实现对未来6 h云图的预测。预报方法的独立样本试验证明,预测结果与实际云图的主要特征基本吻合,尤其在预测云图的大体分布和发展趋势上得到了较好效果。展开更多
基金Sponsored by the National Natural Science Foundation of China(Grant No.51406067)Science and Technology Program of Jilin,China(Grant No.20140203008SF)
文摘In order to evaluate the failure probability of a complicated structure,the structural responses usually need to be estimated by some numerical analysis methods such as finite element method( FEM). The response surface method( RSM) can be used to reduce the computational effort required for reliability analysis when the performance functions are implicit. However,the conventional RSM is time-consuming or cumbersome if the number of random variables is large. This paper proposes a Legendre orthogonal neural network( LONN)-based RSM to estimate the structural reliability. In this method,the relationship between the random variables and structural responses is established by a LONN model. Then the LONN model is connected to a reliability analysis method,i.e. first-order reliability methods( FORM) to calculate the failure probability of the structure.Numerical examples show that the proposed approach is applicable to structural reliability analysis,as well as the structure with implicit performance functions.
基金National Natural Science Foundation of China(Nos.11262014,11962021 and 51965051)Inner Mongolia Natural Science Foundation,China(No.2019MS05064)+1 种基金Inner Mongolia Earthquake Administration Director Fund Project,China(No.2019YB06)Inner Mongolia University of Technology Foundation,China(No.2020015)。
文摘Aiming at the reliability analysis of small sample data or implicit structural function,a novel structural reliability analysis model based on support vector machine(SVM)and neural network direct integration method(DNN)is proposed.Firstly,SVM with good small sample learning ability is used to train small sample data,fit structural performance functions and establish regular integration regions.Secondly,DNN is approximated the integral function to achieve multiple integration in the integration region.Finally,structural reliability was obtained by DNN.Numerical examples are investigated to demonstrate the effectiveness of the present method,which provides a feasible way for the structural reliability analysis.
文摘依据6 h T213数值预报产品的资料,采用EOF展开和人工神经网络等方法,对卫星云图短时预报方法进行研究。首先对卫星云图灰度值样本序列进行EOF展开,将提取出来的时间系数作为建模的预报量,以数值预报产品的物理量场作为预报因子,建立人工神经网络预测模型。将预报得到的时间系数与空间特征向量进行时空反演,实现对未来6 h云图的预测。预报方法的独立样本试验证明,预测结果与实际云图的主要特征基本吻合,尤其在预测云图的大体分布和发展趋势上得到了较好效果。
