[1]张小红,王慧琴,于洪磊,等.基于灰色相关分析的GRFM倾斜量预测模型[J].西安建筑科技大学学报(自然科学版),2016,48(06):919-924.[doi:10.15986/j.1006-7930.2016.06.023]
　ZHANG Xiaohong,WANG Huiqin,YU Honglei,et al.GRFM forecasting mode of inclination based on the grey relation analysis [J].J. Xi’an Univ. of Arch. & Tech.(Natural Science Edition),2016,48(06):919-924.[doi:10.15986/j.1006-7930.2016.06.023]
点击复制

基于灰色相关分析的GRFM倾斜量预测模型()
分享到：

西安建筑科技大学学报（自然科学版）[ISSN:1006-7930/CN:61-1295/TU]

卷:
48
期数:
2016年06期
页码:
919-924
栏目:
出版日期:
2016-12-31

文章信息/Info

Title:
GRFM forecasting mode of inclination based on the grey relation analysis
文章编号:
1006-7930(2016)06-0919-06
作者:
张小红1; 2; 王慧琴1; 3; 于洪磊3; 高大峰4; 王 展5
1．西安建筑科技大学管理学院，陕西 西安710055；2．西安科技大学通信与信息工程学院，陕西 西安 710054；3．西安建筑科技大学信息与控制工程学院，陕西 西安 710055；4．西安建筑科技大学土木工程学院，陕西 西安 710055；5．陕西省文物保护研究院，陕西 西安 710075
Author(s):
ZHANG Xiaohong 1; 2;  WANG Huiqin1; 3;  YU Honglei3;  GAO Dafeng4;  WANG Zhan5
1.School of Management, Xi’an Univ. of Arch. & Tech., Xi’an 710055, China;2. School of Communication&Information Engineering, Xi’an Univ. of Scie.& Tech.,Xi’an 710054, China;3. School of Information Control Engineering, Xi’an Univ. of Arch. & Tech. ,Xi’an 710055,China4. School of Civil Engineering, Xi’an Univ. of Arch. & Tech. ,Xi’an 710055,China;5.Shanxi Provincial Institute of Cultural Relics Proteciton
关键词:
相关性分析; 灰色模型; 径向基神经网络; 预测模型; 砖石古塔
Keywords:
coherence analysis;  grey system;  radial basis function neural network;  forecasting model;  masonry pagodas
分类号:
TU196+.4
DOI:
10.15986/j.1006-7930.2016.06.023
文献标志码:
A
摘要:
针对时间序列的动态性、相关性、小样本性、非线性等特征，利用灰色模型的小样本适用性和神经网络的预测高精度等性能，提出了一种基于相关分析的灰色神经网络组合预测模型．首先，基于灰色相关理论定量分析了倾斜量与沉降观测指标时间序列之间的相关度；然后，采用GM(1,1)模型对原始序列累加求和，降低各因素原始数据的噪声干扰；利用优化径向基神经网络(Radial Basis Function，RBF)多步拟合训练，其中心点和扩展系数初值采用蚁群算法进行优化．最后，将该模型应用到了砖石古塔的倾斜量预测中，设计了沉降综合指数，通过计算，该指数与倾斜量的灰色相关度为0.789 1，采用该模型对某古塔倾斜量进行了预测，平均相对误差为9.056%．实验结果表明，该模型对小样本、非线性的时间序列预测具有高精度和有效性，为古建筑保护中变形预测提供了理论和实践经验．
Abstract:

Considering time series with dynamic, small sample size, relativity, nonlinearity characteristicses, a new grey radial basis function (RBF)neural network forecasting model based on correlation analysis is proposed,which is based on the capability of grey model fitting to the small sample data and the properties of neural network with high prediction precision. At first, the new model analyses the correlation of the inclination and settlements time series quantitatively. And then, generated the accumulation sequences of the original time series using GM(1,1) model, which reduced the noise of the original data; the result is input into the radial basis function neural network, the center pointer and expansion coefficient of which is optimized by ant colony algorithm to forecast the inclination quantity with multi-steps.At last, the new model is applied in small goose pagoda , after the grey correlation of inclination and the inhomogeneous settlement index designed is calculated, the inclination quantity is forecasted with the average relative error of 9.056%.The experimental results show that the proposed model has the capability of the good precision and effectiveness, which supplies the theoretical and practical experience for the ancient building protection. 


参考文献/References:

[1] GULLINO P, LARCHER F. Integrity in UNESCO world heritage sites: A comparative study for rural lanscapes [J]. Journal of Cultural Heritage, 2013 ,14(3): 389-395.

[2] ELISA F.,FRANCO S.,SIMONE B. et al. An experi- mental fixture for continuous monitoring of electrical effects in moist masonry walls[J].Construction and Building Materials,2013,25(4):2023-2029.

[3] 李战胜. 复合地基上建筑物沉降发展规律研究[D].郑州:郑州大学,2007.

LI Zhansheng. Study on the development of building settlement on composite foundation[D].Zhengzhou: Zhengzhou University, 2007.

[4] 杨涛, 李国维, 杨伟清. 基于双曲线法的分级填筑路堤沉降预测[J].岩土力学, 2004,25(10):1551-1554.

YANG Tao, LI Guowei, YANG Weiqing. Prediction of the settlement of embankment with the classification based on the hyperbolic method [J]. Rock and Soil Mechanics, 2004,25 (10): 1551-1554

[5] KOUKOUTAS S P, SOFIANOS A I. Settlements due to single and twin tube urban EPB shield tunnelling[J]. Geotechnical and Geological Engineering,2015,33(3):487- 510.

[6] CHEN Ping, XIE Xiaoning, CHEN Te. Seismic risk assessment of ancient timber-structure[J]. World Earthquake Engineering, 2015, 3(1): 16-21 .

[7] 徐峰, 汪洋, 杜娟, 等. 基于时间序列分析的滑坡位移预测模型研究[J].岩石力学与工程学报,2011(4):746- 751.

XU Feng, WANG Yang, DU Juan, et al. Study of displacement prediction model of landslide based on time series analysis[J]. Journal of Rock Mechanics and Engineering, 2011(4): 746- 751.

[8] 王智磊, 孙红月, 尚岳全. 基于地下水位变化的滑坡预测时序分析[J].岩石力学与工程学报,2011,11:2276- 2284 .

WANG Zhilei, SUN Hongyue, SHANG Yuequan. Time series analysis of landslide prediction based on groundwater level variation[J]. Journal of Rock Mechanics and Engineering, 2011, 11: 2276 - 2284.

[9] POURTAGHI, YAGHIN L M A A. Wavenet ability assessment in comparison to ANN for predicting them aximum surface settlement caused by tunneling[J]. Tunnelling and Underground Space Technology, 2012,28(1): 257-271

[10] AZADI M, POURAKBA S, KASHFIC Ali. Assessment of optimum settlement of structure adjacent urban tunnel by using neural network methods[J]. Tunnelling and Underground Space Technology, 2013, 37(5):1-9.

[11] FALAMARZIA Y, PALIZDANA N, HUANGB Y F, et al . Estimating evapo-transpiration from temperature and wind speed data using artificial and wavelet neural networks [J]. Agricultural Water Management , 2016 , 164(5):26-36.

[12] FALAMARZIA Yashar, PALIZDANA Narges, HUANGB Y F, et al. Estimating evapo-transpiration from temperature and wind speed data using artificial and wavelet neural networks[J]. Agricultural Water Management, 2016,164(5):26-36.

[13] OCAK I, SEKER S E. Calculation of surface settlements caused by EPBM tunneling using artificial neural network, SVM, Gaussian processes[J]. Environmental Earth Sciences, 2013, 26(1):257-266

[14] 高宁, 潘传姣, 李建刚. 深埋隧道围岩变形预测的非线性组合模型[J]. 金属矿山, 2014(8):45-48.

GAO Ning, PAN Chuanjiao, LI Jiangang. Non-linear combination forecast model to predict Surrounding rock deformation in deep buried tunnels [J]. Metal Mine, 2014 (8):45-48.

[15] AHMADI MA, ZENDEHBOUDI S, LOHI A, et al. Reservoir permeability prediction by neural networks combined with hybrid genetic algorithm and particle swarm optimization[J]. Geophysical Prospecting, 2013,61(3): 582-598.

[16] 李小珠, 高大峰, 吴健康. 小雁塔的抗震性能评估.水利与建筑工程学报,2009,07(2):33-35.

LI Xiaozhu, GAO Dafeng, WU Jiankang. Evaluation on seismic performance of small goose pagod[J].Journal of Water Resources and Architechtural Engineering, 2009, 07(2):33-35.

相似文献/References:

[1]于 澍.基于多灰色预测模型对比的高速公路路基沉降量预测[J].西安建筑科技大学学报(自然科学版),2013,45(02):233.[doi:10.15986/j.1006-7930.2013.02.013]
　YU Shu.Highway roadbed settlement prediction based on comparison of gray prediction model[J].J. Xi’an Univ. of Arch. & Tech.(Natural Science Edition),2013,45(06):233.[doi:10.15986/j.1006-7930.2013.02.013]
[2]代慧娟,白国良,王 博,等.近断层滑冲型地震动频谱特性及强度指标研究[J].西安建筑科技大学学报(自然科学版),2013,45(03):383.[doi:10.15986/j.1006-7930.2013.03.014]
　DAI Hui-juan,BAI Guo-liang,WANG Bo,et al.Study on spectral characteristics and intensity indices ofnear-fault fling-step-type ground motions[J].J. Xi’an Univ. of Arch. & Tech.(Natural Science Edition),2013,45(06):383.[doi:10.15986/j.1006-7930.2013.03.014]
[3]陈君,杨东援,赵清梅.公交线路选择的相关性规律和换乘总量计算模型[J].西安建筑科技大学学报(自然科学版),2017,49(06):835.[doi:10.15986/j.1006-7930.2017.06.010]
　CHEN Jun,YANG Dongyuan,ZHAO Qingmei.Correlation law for bus routes selection and total transfer volume calculation model[J].J. Xi’an Univ. of Arch. & Tech.(Natural Science Edition),2017,49(06):835.[doi:10.15986/j.1006-7930.2017.06.010]
[4]石媛,闫增峰.寒冷地区三甲医院建筑能耗评价指标研究[J].西安建筑科技大学学报(自然科学版),2019,51(01):140.[doi:10.15986/j.1006-7930.2019.01.022]
　SHI Yuan,YAN Zenfeng.Study on energy consumption evaluation indicators for Grade-A tertiary hospitals[J].J. Xi’an Univ. of Arch. & Tech.(Natural Science Edition),2019,51(06):140.[doi:10.15986/j.1006-7930.2019.01.022]

备注/Memo

备注/Memo:
收稿日期：2016-04-15            修改稿日期：2016-11-22

基金项目：国家教育部归国留学人员科技支撑基金资助项目(K05055)；西安市碑林区科技计划基金资助项目(GX1614)

作者简介：张小红(1978－)，女，博士生，讲师，主要从事物联网应用与数据分析方面的研究．E-mail: 447973560@qq.com

更新日期/Last Update: 2017-02-06