|
|
- 2018
利用优化的组合核相关向量机算法构建地表下沉预测模型
|
Abstract:
为了提高地下开采地表下沉预测结果的精度及可靠性，提出了基于混合智能优化算法（hybrid intelligent optimization algorithm，HIOA）与组合核相关向量机（multi-kernel relevance vector machine，MK-RVM）的地下开采地表下沉预测方法。首先，分别构建HIOA与MK-RVM算法，并利用HIOA优化MK-RVM的参数。然后，采用优化后的MK-RVM构建地表下沉几何参数预测模型和动态下沉预测模型。最后，利用以上模型对上山移动角、下山移动角、中心移动角、地表最大下沉及动态下沉进行预测，并分析预测结果的精度及可靠性。实验结果表明，该方法的精度与可靠性较单一核函数相关向量机与支持向量机有较大改善
| [1] | Ding D X, Zhang Z J, Bi Z W. A New Approach to Predicting Mining Induced Surface Subsidence[J].<em>Journal of Central South University of Technology</em>, 2006, 13(4):438-444 |
| [2] | Radosavljevic J, Klimenta D, Jevtic M, et al. Optimal Power Flow Using a Hybrid Optimization Algorithm of Particle Swarm Optimization and Gravitational Search Algorithm[J]. <em>Electric Power Components & Systems</em>, 2015, 25(5):1958-1970 |
| [3] | Kratzsch H. Mining Subsidence Engineering[M]. New York:Springer, 1983 |
| [4] | State Coal Industry Bureau. Buildings, Water Bodies and Railways and the Main Shaft Coal Pillar Retaining and Press Coal Mining Regulations[M]. Beijing:China Coal Industry Publishing Home, 2000(国家煤炭工业局. 建筑物、水体、铁路及主要井巷煤柱留设与压煤开采规程[M]. 北京:煤炭工业出版社, 2000) |
| [5] | Guo Zengzhang, Chai Huabin.Coal Mining Subsi-dence[M].Beijing:China Coal Industry Publishing Home,2013(郭增长,柴华彬.煤矿开采沉陷学[M].北京:煤炭工业出版社,2013) |
| [6] | Li M W, Geng J, Han D F, et al. Ship Motion Prediction Using Dynamic Seasonal RvSVR with Phase Space Reconstruction and the Chaos Adaptive Efficient FOA[J].<em>Neurocomputing</em>,2016, 174(3):661-680 |
| [7] | Wei Bowen,Xiong Wei,Li Huokun, et al. Dam Deformation Forecasting of Leapfrog Combined Model Merging Residual Errors of Chaos[J]. <em>Geomatics and Information Science of Wuhan University</em>, 2016,41(9):1272-1278(魏博文,熊威,李火坤,等. 融合混沌残差的大坝位移蛙跳式组合预报模型[J]. 武汉大学学报·信息科学版,2016,41(9):1272-1278) |
| [8] | Lü W Y, Zhang Z H. Surface Subsidence Prediction Model of Coal Mining Based on Chaos Optimization and BP Neural Network[J]. <em>Electronic Journal of Geotechnical Engineering, </em>2015, 20(12):4401-4411 |
| [9] | Blekas K, Likas A. Sparse Regression Mixture Modeling with the Multi-kernel Relevance Vector Machine[J]. <em>Knowledge and Information Systems</em>, 2014, 39(2):241-264 |
| [10] | Li Lei.Research of Monitoring Data Analysis and Prediction on the Settlement of Goaf Surface[D]. Xi'an:Xi'an University of Architecture and Technology, 2012(李磊. 采空区地表沉降监测数据分析与预测研究[D].西安:西安建筑科技大学,2012) |
| [11] | Li L, Wu K, Zhou D W. Extraction Algorithm of Mining Subsidence Information on Water Area Based on Support Vector Machine[J]. <em>Environmental Earth Sciences</em>, 2014, 72(10):3991-4000 |
| [12] | Fan Geng,Ma Dengwu. Probabilistic Prediction Method for Aeroengine Performance Parameters Based on Combined Optimum Relevance Vector Machine[J]. <em>Acta Aeronautica et Astronautica Sinica</em>, 2013, 34(9):2110-2121(范庚, 马登武. 基于组合优化相关向量机的航空发动机性能参数概率预测方法[J]. 航空学报, 2013, 34(9):2110-2121) |
| [13] | Khosravi A, Nahavandi S, Creighton D, et al. Comprehensive Review of Neural Network-Based Prediction Intervals and New Advances[J].<em>IEEE Transactions on Neural Networks</em>, 2011,22(9):1341-1356 |
| [14] | Yu Shiwei, Wei Yiming, Zhu Kejun. Hybrid Optimization Algorithms Based on Particle Optimization and Gene-tic Algorithm Swarm[J]. <em>Systems Engineering and Electronics</em>, 2011,33(7):1647-1652(於世为,魏一鸣,诸克军. 基于粒子群-遗传的混合优化算法[J]. 系统工程与电子技术,2011,33(7):1647-1652) |
| [15] | Zhang Zhijun. Study on PSA-ANFIS and Its Application in Prediction the Mine Geotechnical Enginee-ring Disaster[D]. Changsha:Central South University, 2008(张志军. PSA-ANFIS方法及其在矿山岩土工程灾害预测中的应用[D].长沙:中南大学,2008) |
