[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]
点击复制

基于图像识别和神经网络的大跨度结构风荷载模拟()
分享到：

《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
29
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.

相似文献/References:

[1]琚新刚,郭海鸥,郭敏..基于径向基神经网络的乙醇气体检测仪仿真分析[J].郑州大学学报(工学版),2010,31(03):61.[doi:10.3969/j.issn.1671-6833.2010.03.016]
　Ju Xingang,Guo Haiou,Guo Min.Simulation analysis of ethanol gas detector based on radial basis neural network[J].Journal of Zhengzhou University (Engineering Science),2010,31(04):61.[doi:10.3969/j.issn.1671-6833.2010.03.016]
[2]曾庆山,全书鹏,靳志强..融合BP神经网络与ARIMA的短时交通流预测[J].郑州大学学报(工学版),2011,32(04):60.[doi:10.3969/j.issn.1671-6833.2011.04.015]
[3]刘景艳,李玉东,杨晓邦..遗传神经网络在齿轮故障诊断中的应用[J].郑州大学学报(工学版),2012,33(03):36.[doi:10.3969/j.issn.1671-6833.2012.03.009]
　LIU Jingyan,LI Yudong,YANG Xiaobang.Application of Genetic Neural Network to Gear Fault Diagnosis[J].Journal of Zhengzhou University (Engineering Science),2012,33(04):36.[doi:10.3969/j.issn.1671-6833.2012.03.009]
[4]高攀科,谢永利..隧道软弱围岩的改进BP神经网络位移反分析[J].郑州大学学报(工学版),2013,34(01):23.[doi:10.3969/j.issn.1671-6833.2013.01.006]
　GA0 Pan-ke,XIE Yong-li.Displacement Back Analysis of Tunnels in Soft and Weak RocksBased on Improved BP Neural Network Method[J].Journal of Zhengzhou University (Engineering Science),2013,34(04):23.[doi:10.3969/j.issn.1671-6833.2013.01.006]
[5]任松,姜德义,杨春和..基于遗传算法的浅埋隧道开挖地表沉降神经网络预测[J].郑州大学学报(工学版),2006,27(03):46.[doi:10.3969/j.issn.1671-6833.2006.03.011]
　Ren Song,JIANG Deyi,Yang Chunhe.Prediction of surface settlement neural network for shallow buried tunnel excavation based on genetic algorithm[J].Journal of Zhengzhou University (Engineering Science),2006,27(04):46.[doi:10.3969/j.issn.1671-6833.2006.03.011]
[6]李清富,成子桥,刘建民..混凝土结构剩余寿命的预测[J].郑州大学学报(工学版),2003,24(01):11.[doi:10.3969/j.issn.1671-6833.2003.01.003]
　LI Qingfu,Chengzi Bridge,Liu Jianmin.Prediction of the remaining life of concrete structures[J].Journal of Zhengzhou University (Engineering Science),2003,24(04):11.[doi:10.3969/j.issn.1671-6833.2003.01.003]
[7]张鸿河,关惠玲..基于包络分析的自行火炮变速箱故障诊断研究[J].郑州大学学报(工学版),2003,24(03):91.[doi:10.3969/j.issn.1671-6833.2003.03.023]
[8]冯冬青,张希平..基于神经网络的自学习模糊控制[J].郑州大学学报(工学版),2003,24(04):6.[doi:10.3969/j.issn.1671-6833.2003.04.002]
　Feng Dongqing,Zhang Xiping.Self-learning fuzzy control based on neural network[J].Journal of Zhengzhou University (Engineering Science),2003,24(04):6.[doi:10.3969/j.issn.1671-6833.2003.04.002]
[9]王东云,郭基凤..智能加工单元及调度问题的研究[J].郑州大学学报(工学版),1999,20(03):22.[doi:10.3969/j.issn.1671-6833.1999.03.008]
　WANG Dongyun,Guo Jifeng.Research on intelligent processing unit and scheduling problem[J].Journal of Zhengzhou University (Engineering Science),1999,20(04):22.[doi:10.3969/j.issn.1671-6833.1999.03.008]
[10]王明东,杨岙迪,李龙好,等.基于VSG 下垂优化控制的新能源电力系统惯性提升[J].郑州大学学报(工学版),2024,45(03):127.[doi:10. 13705/ j. issn. 1671-6833. 2024. 03. 005]
　WANG Mingdong,YANG Aodi,LI Longhao,et al.Inertia Lifting of New Energy Power System Based on VSG Droop Optimal Control[J].Journal of Zhengzhou University (Engineering Science),2024,45(04):127.[doi:10. 13705/ j. issn. 1671-6833. 2024. 03. 005]

更新日期/Last Update: 1900-01-01