基于快速极限学习机和差分进化的机场噪声预测模型

徐涛; 郭威; 吕宗磊

doi:10.11999/JEIT150986

基于快速极限学习机和差分进化的机场噪声预测模型

doi: 10.11999/JEIT150986

2.
(中国民航大学计算机科学与技术学院天津 300300) ②(南京航空航天大学计算机科学与技术学院南京 210016) ③(盐城师范学院信息工程学院盐城 224002)

基金项目:

国家自然科学基金重点项目(61139002)，国家科技支撑计划课题(2014BAJ04B02)，中央高校基本科研业务费专项资金(3122014D032)，中国民航信息技术科研基地开放基金课题(CAAC- ITRB-201401)

计量
- 文章访问数: 1341
- HTML全文浏览量: 171
- PDF下载量: 376
- 被引次数: 0
出版历程
- 收稿日期: 2015-09-06
- 修回日期: 2016-01-20
- 刊出日期: 2016-06-19

Prediction Model of Airport?Noise Based on Fast Extreme Learning Machine and Differential Evolution

Funds:

The Key Program of the National Natural Science Foundation of China (61139002), The National Key Technology Research and Development Program of the Ministry of Science and Technology of China (2014BAJ04B02), The Fundamental Research Funds for the Central Universities of Ministry of Education of China (3122014D032), The Open Project Foundation of Information Technology Research Base of Civil Aviation Administration of China (CAAC-ITRB-201401)

摘要

摘要: 该文针对传统机场噪声预测模型存在的建模成本高、实用性差的不足，引入时间序列相空间重构理论，提出一种新的基于快速极限学习机和差分进化算法的机场噪声一体化预测模型。该模型利用相空间重构理论对机场噪声时间序列进行重构，并使用快速极限学习机对重构的相空间矢量进行学习建模，同时采用改进的差分进化算法实现对重构参数和模型参数的同步优化选择，整个建模过程简洁高效，无需人工干预。实验结果表明，该一体化预测模型能较好地跟踪机场噪声的变化趋势，且具有较同类模型更小的预测误差。
- 机场噪声预测 /
- 快速极限学习机 /
- 差分进化 /
- 相空间重构
Abstract: Traditional airport noise prediction models are insufficient for their high modeling cost and poor practicability. In this paper, the time series phase space reconstruction theory is introduced, and a novel integrated airport noise prediction model based on fast extreme learning machine and differential evolution is proposed. In the proposed model, the airport noise time series is reconstructed based on the phase space reconstruction theory, and the fast extreme learning machine is used to model the reconstructed phase space vector. Meanwhile, an improved differential evolution algorithm is adopted to search for the optimal parameter combination of phase space reconstruction parameter and model parameter simultaneously. The whole modeling process of the integrated prediction model is very simple and?efficient without any manual?intervention. Experimental results demonstrate that the proposed model can track the variation tendency of airport noise well and can achieve much more accurate prediction results than its counterparts.
- Airport noise prediction /
- Fast extreme learning machine /
- Differential evolution /
- Phase space reconstruction

HTML全文

参考文献(22)

SARI D, OZKURT N, AKDAG A, et al. Measuring the levels of noise at the Atatrk Airport and comparisons with model simulations[J]. Science of The Total Environment, 2014, 482(6): 472-479. doi: 10.1016/j.scitotenv.2013.07.091.

OZKURT N, HAMAMCI S F, and SARI D. Estimation of airport noise impacts on public health. A case study of Adnan Menderes Airport[J]. Transportation Research Part D: Transport and Environment, 2015, 36(5): 152-159. doi: 10. 1016/j.trd.2015.02.002.

陈海燕, 杨冰欣, 徐涛, 等. 基于模糊支持向量回归的机场噪声预测[J]. 南京航空航天大学学报, 2013, 45(5): 722-726.

CHEN Haiyan, YANG Bingxin, XU Tao, et al. Airport noise prediction based on fuzzy support vector regression[J]. Journal of Nanjing University of Aeronautics Astronautics, 2013, 45(5): 722-726.

FILIPPONE A and BERTSCH L. Comparison of aircraft noise models with flyover data[J]. Journal of Aircraft, 2014, 51(3): 1043-1047. doi: 10.2514/1.C032368.

GUARNACCIA C, QUARTIERI J, TEPEDINO C, et al. An analysis of airport noise data using a non-homogeneous Poisson model with a change-point[J]. Applied Acoustics, 2015, 91(4): 33-39. doi: 10.1016/j.apacoust.2014.12.002.

FILIPPONE A. Aircraft noise prediction[J]. Progress in Aerospace Sciences, 2014, 68(7): 27-63. doi: 10.1016/ j.paerosci.2014.02.001.

徐涛, 燕宪金, 杨国庆. 基于神经网络集成的单个飞行事件噪声预测模型[J]. 中国环境科学, 2014, 34(2): 539-544.

XU Tao, YAN Xianjin, and YANG Guoqing. Prediction model of noise event for single flight based on neural network ensemble[J]. China Environmental Science, 2014, 34(2): 539-544.

徐涛, 杨奇川, 吕宗磊. 一种基于动态集成学习的机场噪声预测模型[J]. 电子与信息学报, 2014, 36(7): 1631-1636. doi: 10.3724/SP.J.1146.2013.01410.

XU Tao, YANG Qichuan, and LU Zonglei. A prediction model of airport noise based on the dynamic ensemble learning[J]. Journal of Electronics Information Technology, 2014, 36(7): 1631-1636. doi: 10.3724/SP.J.1146.2013.01410.

STORN R and PRICE K. Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces[J]. Journal of Global Optimization, 1997, 11(4): 341-359. doi: 10.1023/A:1008202821328.

HUANG G B, ZHU Q Y, and Siew C K. Extreme learning machine: theory and applications[J]. Neurocomputing, 2006, 70(1): 489-501. doi: 10.1016/j.neucom.2005.12.126.

HOUGH P D and VAVASIS S A. Complete orthogonal decomposition for weighted least squares[J]. SIAM Journal on Matrix Analysis and Applications, 1997, 18(2): 369-392. doi: 10.1137/S089547989528079X.

GOLUB G H and VAN LOAN C F. Matrix Computations[M]. Baltimore: JHU Press, 2012: 274-283.

PACKARD N H, CRUTCHFIELD J P, FARMER J D, et al. Geometry from a time series[J]. Physical Review Letters, 1980, 45(9): 712-716. doi: 10.1103/PhysRevLett.45.712.

TAKENS F. Detecting strange attractors in turbulence[J]. Dynamical Systems and Turbulence, 1981, 898(1): 366-381.

KAYACAN E, ULUTAS B, and KAYNAK O. Grey system theory-based models in time series prediction[J]. Expert Systems with Applications, 2010, 37(2): 1784-1789. doi: 10.1016/j.eswa.2009.07.064.

ZHANG N, WILLIAMS C, and BEHERA P. Water quantity prediction using least squares support vector machines (LS-SVM) Method[J]. Journal of Systemics, Cybernetics and Informatics, 2014, 2(4): 53-58.

AFSHAR K and BIGDELI N. Data analysis and short term load forecasting in Iran electricity market using singular spectral analysis (SSA)[J]. Energy, 2011, 36(5): 2620-2627. doi: 10.1016/j.energy.2011.02.003.

温冬琴, 王建东. 基于奇异谱分析的机场噪声时间序列预测模型[J]. 计算机科学, 2014, 41(1): 267-270.

WEN Dongqin and WANG Jiandong. Prediction model for airport-noise time series based on SSA[J]. Computer Science, 2014, 41(1): 267-270.

施引文献

资源附件(0)

访问统计