[1]孙芳锦,张大明,殷志祥..基于图像识别和神经网络的大跨度结构风荷载模拟[J].郑州大学学报(工学版),2008,29(04):116-119.[doi:10.3969/j.issn.1671-6833.2008.04.027]
　SUN Fangjin,ZHANG Daming,Yin Zhixiang.Wind load simulation of large-span structure based on image recognition and neural network[J].Journal of Zhengzhou University (Engineering Science),2008,29(04):116-119.[doi:10.3969/j.issn.1671-6833.2008.04.027]
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基于图像识别和神经网络的大跨度结构风荷载模拟()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
29卷
期数:
2008年04期
页码:
116-119
栏目:
出版日期:
1900-01-01

文章信息/Info

Title:
Wind load simulation of large-span structure based on image recognition and neural network
作者:
孙芳锦; 张大明; 殷志祥.
辽宁工程技术大学土木建筑工程学院,辽宁,阜新,123000, 辽宁工程技术大学技术与经济学院,辽宁,阜新,123000
Author(s):
SUN Fangjin; ZHANG Daming; Yin Zhixiang
关键词:
图像识别技术;  神经网络;  风荷栽模拟;  图像信息;  计算效率
Keywords:
DOI:
10.3969/j.issn.1671-6833.2008.04.027
文献标志码:
A
摘要:
将图像识别技术和神经网络(ANN)系统相结合,给出了大跨度结构风荷载的模拟方法.采用递归图和特征脸识别两种图像识别技术,将风速时间数值序列转化为图像信息.然后将大量风速时间序列转换为维数较少的向量Ω,再结合多层ANN体系得到简化的神经网络模型,预测出空间各点的风速时程.最后将其应用到一大跨度结构的风荷载模拟中,结果不仅与目标值符合良好,而且可以减少神经网络的层数,大大提高运算速度和结果的可信度.
Abstract:
Combining image recognition technology and neural network (ANN) system, a simulation method for wind load of large-span structure is given. Two image recognition technologies, recursive graph and feature face recognition, are used to convert the wind speed time numerical series into image information. Then, a large number of wind speed time series are converted into vector Ω with fewer dimensions, and then combined with the multi-layer ANN system to obtain a simplified neural network model to predict the wind speed time history of each point in space. Finally, it is applied to the wind load simulation of a large-span structure, and the results not only conform well with the target value, but also reduce the number of layers of the neural network, and greatly improve the operation speed and the credibility of the results.

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更新日期/Last Update: 1900-01-01