Optimization of Gaussian Process Model Based on Artificial Fish School Algorithm and Its Application Analysis

doi:10.13474/j.cnki.11-2246.2018.0031

Abstract

Abstract:

Based on the Gaussian process,the traditional conjugate gradient method is used to search the hyper-parameters, which has the disadvantages of strong dependence on the initial value and obtain local optimal solution. Artificial fish swarm algorithm is used to optimize the hyper-parameters intelligently, and the Gaussian process model based on the artificial fish swarm algorithm is used to predict and analysize the deformation of deformable body. Through the tunnel and foundation pit examples to compare the calculated results, NN kernel function is the best predictor of NN, SE and RQ, the average relative error is 0.69% and 1.06%. The results show that the prediction accuracy of the hyper-parameters optimization model are greatly improved, and the hyper-parameters solving of the Gaussian process algorithm themselves are improved and the effect is obvious.

Key words: artificial fish swarm algorithm, Gaussian process, rolling prediction method, deformation monitoring

CLC Number:

P208

QIU Xiaomeng, ZHOU Shijian, WANG Fengwei, OUYANG Liangyou. Optimization of Gaussian Process Model Based on Artificial Fish School Algorithm and Its Application Analysis[J]. 测绘通报, 2018, 0(1): 157-160,164.

References

[1] 王卫东,何晖.回归分析在隧道拱顶沉降监测中的应用[J].科技信息,2011(7):271-272.
[2] 袁金荣,赵福勇.基坑变形预测的时间序列分析[J].土木工程学报,2001,34(6):55-59.
[3] 潘国荣,王穗辉.深基坑事故隐患的灰色预测[J].同济大学学报(自然科学版),1999,27(3):319-322.
[4] 陶津,刘燕,贺可强.神经网络在深基坑工程变形预测中的应用研究[J].青岛理工大学学报,2005,26(6):39-42.
[5] 高永刚,岳建平,石杏喜.支持向量机在变形监测数据处理中的应用[J]. 水电自动化与大坝监测,2005,29(5):36-39.
[6] 李晓蕾,刘睿,田永瑞,等. 基于灰色预测的空间多点残差修正模型研究[J]. 大地测量与地球动力学,2010,30(5):125-128.
[7] 霍玉华. 隧道围岩变形量预测的灰色模型应用比较研究[J]. 北京交通大学学报,2006,30(4):42-45.
[8] 肖智旺,钟登华. 基于RBF神经网络算法的连拱隧道围岩变形预测方法研究[J]. 中国工程科学,2008,10(7):77-81.
[9] 熊志化,张继承,邵惠鹤. 基于高斯过程的软测量建模[J].系统仿真学报,2005,17(4):793-794,800.
[10] WILLIAMS C K,RASMUSSEN C E.Gaussian Processes for Regression[J]. Advances in Neural Information Processing Systems,1996,8(6):514-520.
[11] 苏国韶,燕柳斌,张小飞,等. 基坑位移时间序列预测的高斯过程方法[J]. 广西大学学报(自然科学版),2007,32(2):223-226.
[12] 罗亦泳,姚宜斌,张立亭,等. 基于高斯过程的GPS高程转换模型[J].测绘通报,2015(11):11-14.
[13] 刘开云,方昱,刘保国,等. 隧道围岩变形预测的进化高斯过程回归模型[J]. 铁道学报,2011,33(12):101-106.
[14] 徐冲,刘保国,刘开云,等. 基于粒子群-高斯过程回归耦合算法的滑坡位移时序分析预测智能模型[J]. 岩土力学,2011,32(6):1669-1675.
[15] 何志昆,刘光斌,赵曦晶,等. 高斯过程回归方法综述[J]. 控制与决策,2013,28(8):1121-1129,1137.
[16] 李晓磊. 一种新型的智能优化方法——人工鱼群算法[D].杭州:浙江大学,2003.
[17] 刘开云,刘保国,徐冲. 基于遗传-组合核函数高斯过程回归算法的边坡非线性变形时序分析智能模型[J]. 岩石力学与工程学报,2009,28(10):2128-2134.
[18] 雷鸣. 基于高斯过程的变形预测算法研究[D].南昌:东华理工大学,2013.
[19] 尹晖,周晓庆,张晓鸣. 非等间距多点变形预测模型及其应用[J]. 测绘学报,2016,45(10):1140-1147.