[1]李越超.基于QPSO-LSSVM的边坡变形预测[J].山地学报,2015,(03):374.
 LI Yuechao.Forecasting of Slope Displacement Based on QPSO-LSSVM Method[J].Mountain Research,2015,(03):374.
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基于QPSO-LSSVM的边坡变形预测()
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《山地学报》[ISSN:1008-2186/CN:51-1516]

卷:
期数:
2015年03期
页码:
374
栏目:
山地灾害
出版日期:
2015-06-01

文章信息/Info

Title:
Forecasting of Slope Displacement Based on QPSO-LSSVM Method
作者:
李越超;
中国铁建十一局集团城市轨道工程有限公司;
Author(s):
LI Yuechao
Urban Mass Transit Engineering Co.,Ltd of China Railway 11th Construction Bureau Group,Wuhan 430074, China
关键词:
边坡变形变形预测最小二乘支持向量机量子粒子群优化
Keywords:
slope displacement prediction of deformation least square support vector machine quantumbehaved particle swarm optimization
分类号:
P642
文献标志码:
A
摘要:
滑坡变形受外界影响因素作用的机理十分复杂,难以采用简单方法对其进行预测,因此建立快速准确的滑坡预测模型十分重要。采用比一般支持向量机(SVM)预测效果更好且计算速度更快的最小二乘支持向量机(LSSVM)方法,选用RBF核函数对边坡位移时序数据进行训练和预测,并引入量子粒子群算法(QPSO)对LSSVM模型参数γ和σ进行全局寻优,避免了人为选择参数的盲目性,提高了模型的预测精度。将优化模型应用于新滩滑坡和卧龙寺新滑坡的变形预测,并与传统的LSSVM、PSO-LSSVM模型进行预测精度及收敛性对比分析。结果表明,QPSO-LSSVM模型较传统方法在预测精度上有了明显提高,且收敛速度明显加快,说明Q...
Abstract:
The mechanism of slope deformation is complicated, because it is influenced by outside factors. It is difficult to adopt simple method to predict, so establish a fast and accurate slope displacement prediction model is very important. The method of least squares support vector machines (LSSVM) with higher accuracy than standard support vector machines method is used to train and simulation the slope displacementtime data. And the quantumbehaved particle swarm optimization (QPSO) is adopted to optimize the parameters (γ,σ) of LSSVM model in order to avoid artificial arbitrariness and enhance the forecast accuracy. For comparison, the model of QPSO-LSSVM, LSSVM and the traditional SVM are used to forecast the same series displacementtime data of Xintan slope and Wolongsi slope. The results indicate that the QPSO-LSSVM method is much better than traditional method in terms of forecast accuracy and can be well applied to the forecast of displacementtime series.

参考文献/References:

[1] Feng Xiating. Introduction to intelligent rock mechanics [M]. Beijing: Science Press,2000.[冯夏庭. 智能岩石力学导论[M]. 北京:科学出版社, 2000.]
[2] Wu Yiping, Teng Weifu, Li Yawei. Application of grey-neural network model to landslide deformation prediction[J]. Chinese Journal of Rock Mechanics and Engineering, 2007, 26(3): 632-636 [吴益平, 滕伟福, 李亚伟. 灰色-神经网络模型在滑坡变形预测中的应用[J].岩石力学与工程学报, 2007, 26(3): 632-636]
[3] Gao Wei, Feng Xiating. Study on displacement predication of landslide based on grey system and evolutionary neural network [J]. Rock and Soil Mechanics, 2004, 25(4): 514-517 [高玮, 冯夏庭. 基于灰色-进化神经网络的滑坡变形预测研究[J]. 岩土力学, 2004, 25(4): 514- 517]
[4] Zeng Yao, Li Chunfeng. Landslide displacement prediction by using multivariable time series based on RBF neural network [J]. Journal of Yangtze River Scientific Research Institute, 2012, 29(4): 30-34 [曾耀,李春峰. 基于RBF多变量时间序列的滑坡位移预测研究[J]. 长江科学院院报, 2012, 29(4): 30-34]
[5] Tang Lu, Qi Huan. Prediction of landslide based on chaos and neural networks[J].Chinese Journal of Rock Mechanics and Engineering,2003,33(12):1984-1987[唐璐, 齐欢. 混沌和神经网络结合的滑坡预测方法[J]. 岩石力学与工程学报, 2003, 22(12): 1984-1987]
[6] Dong Hui, Fu Helin, Leng Wuming. Kernel design for displacement time series of landslide[J]. Rock and Soil Mechanics, 2008, 29(4): 1087-1092 [董辉, 傅鹤林, 冷伍明. 滑坡位移时序预测的核函数构造[J].岩土力学, 2008, 29(4): 1087-1092]
[7] Xu Fei, Xu Weiya, Wang Ke. Slope stability analysis using least square support vector machine optimized with ant colony algorithm [J]. Journal of Engineering Geology, 2009, 17(2): 253-257[徐飞, 徐卫亚, 王珂. 基于蚁群优化最小二乘支持向量机模型的边坡稳定性分析[J].工程地质学报, 2009, 17(2): 253-257]
[8] Zhao Hongbo, Feng Xiating. Study and application of geneticsupport vector machine for nonlinear displacement time series forecasting[J].Chinese Journal of Geotechnical Engineering,2003,25(4):468-471[赵洪波, 冯夏庭. 非线性位移时间序列预测的进化-支持向量机方法及应用[J].岩土工程学报, 2003, 25(4): 468-471]
[9] Suykens J A K, Vandewalle J. Least squares support vector machine classifiers[J]. Neural Process Letters, 1999, 9(3): 293-299
[10] Kennedy J,Eberhart R,Particle Swarm Optimization[G]//Proc. IEEE International Conference on Neural Networks,IV. Piscataway, NJ:IEEE Service Center, 1995:1942-1948
[11] JunSun, BinFeng, WenboXu. Particle Swarm Optimization with particles having quantum behavior[G]// Congress on Evolutionary Computation, 2004.
[12] He Keqiang, Sun Linna, Wang Sijing. Displacement fractal parameter Hurst index and its application to prediction of debris landslides[J].Chinese Journal of Rock Mechanics and Engineering, 2009, 28(6): 1107- 1115[贺可强,孙林娜,王思敬.滑坡位移分形参数Hurst指数及其在堆积层滑坡预报中的应用[J].岩石力学与工程学报,2009,28(6): 1107-1115]
[13] Liu Yongjian, Zhang Boyou. Application of chaotic timeseries in slope displacement forecasting[J]. Journal of Liaoning Technical University, 2007, 26(1): 74-76[刘勇健, 张伯友. 混沌时间序列在边坡位移预测中的应用[J].辽宁工程技术大学学报, 2007, 26(1): 74-76]

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备注/Memo

备注/Memo:
收稿日期(Received date):2014-06-16;修回日期(Accepted):2014-09-05。
作者简介(Biography):李越超(1987-),男,湖北武汉人,硕士,助理工程师,主要从事岩土体工程性质及边坡稳定性理论研究与应用研究。[Li Yuechao(1987-), male, from Wuhan, Hubei, Master of Engineering, Assistant Engineer, engaged in the research on theory and application of the engineering property and stability of rock and soil. ]E-mail: 411053845 @qq.com
更新日期/Last Update: 1900-01-01