基于小波变换和GM-ARMA的导弹备件消耗预测

赵建忠; 徐廷学; 葛先军; 尹延涛

基于小波变换和GM-ARMA的导弹备件消耗预测

1.
海军航空工程学院兵器科学与技术系, 烟台 264001
2. 海军航空工程学院科研部, 烟台 264001

详细信息

中图分类号: TB 114.3
计量
- 文章访问数: 1530
- HTML全文浏览量: 62
- PDF下载量: 580
- 被引次数: 0
出版历程
- 收稿日期: 2012-05-03
- 网络出版日期: 2013-04-30

Consumption forecasting of missile spare parts based on wavelet transform and revised GM-ARMA model

1.
Department of Ordnance Science and Technology, Naval Aeronautical and Astronautical University, Yantai 264001, China
2. Department of Scientific Research, Naval Aeronautical and Astronautical University, Yantai 264001, China

摘要

摘要: 针对导弹备件消耗呈现"小样本、非平稳"的特点, 为了克服传统预测方法依靠大样本数据进行建模的不足,提出了把基于小波变换和改进GM-ARMA的组合预测方法应用于导弹备件消耗预测的构想.在利用小波分解和其他模型建立组合模型的过程中,提出了先对小波基方程和分解层数2个特征进行参数化,再定量地对所有子模型的特征参数进行统一、综合的评估,以达到建立最佳组合模型的目的;然后对具有平稳特性的高频信息用阻尼最小二乘法优化的ARMA(Autoregressive and Moving Average)模型进行预测,对反映整体趋势体现非平稳的低频信息用背景值优化和数据变换技术改进的GM(1,1)模型进行预测.实例结果表明所提出的组合预测方法大大降低了预测误差,说明了该方法的有效性、可行性和实用性.
- 小波变换 /
- 灰色模型 /
- 自回归移动平均模型 /
- 备件 /
- 消耗预测
Abstract: Aiming at the characteristic of missile spare parts consumption presenting "small sample and non-stationary", and to overcome the deficiency that traditional forecasting method builds models with abundant date sample,the combined forecast method based on wavelet transform and revised GM-ARMA was proposed to carry through consumption forecasting of missile spare parts. In the process of building a hybrid model by combining wavelet decomposition with other forecasting model,first the new parameters of wavelet functions and decomposition levels were introduced, and all the parameters as a whole for the purpose of building an optimal hybrid model were quantitatively estimated. Then the high frequency signals were forecasted with ARMA model optimized by damping least-squares method, and the low frequency was forecasted with improved GM(1,1) model based on background value optimization and transformation. Experimental results show that the combined forecasting method significantly reduces forecast errors and is valid, feasible and useful in terms of forecasting missile spare parts consumption.
- wavelet transform /
- grey model /
- autoregressive and moving average(ARMA) model /
- spare parts /
- consumption forecasting

HTML全文

参考文献(16)

[1]	胡效雷,何祖威.基于GM-ARMA组合模型的年电力需求预测[J].广东电力,2007,20(2):10-13 Hu Xiaolei,He Zuwei.Forecasting about annual electric consumption based on GM-ARMA combined model[J].Guangdong Electric Power,2007,20(2):10-13(in Chinese)
[2]	Amjady N,Keynia F.Short-term load forecasting of power systems by combination of wavelet transform and neuro-evolutionary algorithm[J].Energy,2009(34):46-57
[3]	马林才,许玮珑,刘大学.基于小波变换的城市市区典型路段交通流量预测[J].控制与决策,2011,26(5):789-793 Ma Lincai,Xu Weilong,Liu Daxue.Prediction model of traffic flow along typical roads in city urban district based on wavelet transform[J].Control and Decision,2011,26(5):789-793(in Chinese)
[4]	钱吴永,党耀国.基于振荡序列的GM(1,1)模型[J].系统工程理论与实践,2009,29(3):149-154 Qian Wuyong,Dang Yaoguo.GM(1,1) model of oscillation sequences[J].Systems Engineering Theory & Practice,2009,29(3):149-154(in Chinese)
[5]	Bo L,Yong W.Optimized GM(1,1) grey model based on connotation expression[J].Journal of Grey System,2007(3):133-136
[6]	赵宇哲,武春友.灰色振荡序列GM(1,1)模型及在城市用水中的应用[J].运筹与管理,2010,19(5):155-166 Zhao Yuzhe,Wu Chunyou.GM(1,1) model of grey oscillation sequences and its application to urban water consumption forecasting[J].Oerations Research and Management Science,2010,19(5):155-166(in Chinese)
[7]	Wang M H.Grey-extension method for incipient fault forecasting of oil-immersed power transformer[J].Electric Power Component and Systems,2004,32(10):959-975
[8]	Ives A,Abbott K C,Ziebarth N L.Analysis of ecological time series with ARMA(p,q) model[J].Ecology,2010,91(3):858-871
[9]	Carden E P,Brownjohn J M W.ARMA modeled time-series classification for structural health monitoring of civil infrastructure[J].Mechanical Systems and Signal Processing,2008,22(2):295-314
[10]	Topres J L,Garcl A,Deblas M,et al.Forecast of hourly average wind speed with ARMA models in Navarre[J].Solar Energy,2005(79):65-77
[11]	Kim H J,Shin K S.A hybrid approach based on neural networks and genetic algorithms for detecting temporal patterns in stock markets[J].Applied Soft Computing,2007,7(2):569-576
[12]	Bermudez J D,Segura J V.A decision support system methodology for forecasting of time series based on soft computing[J].Computational Statistics & Data Analysis,2006,51(1):177-191
[13]	何永沛.ARMA模型参数估计算法改进及在股票预测中的应用[J].重庆工学院学报:自然科学版,2009,23(2):109-112 He Yongpei.Improved algorithm of parameter estimating for ARMA model and its application in the stock markets forecasting [J].Journal of Chongqing Institute of Technology:Natural Science,2009,23(2):109-112(in Chinese)
[14]	吴朝阳.改进的灰色模型与ARMA模型的股指预测[J].智能系统学报,2010,5(3):277-281 Wu Zhaoyang.Forecasting stock index based on revised grey model and ARMA model[J].CAAI Transactions on Intelligent Systems,2010,5(3):277-281(in Chinese)
[15]	吴朝阳.小波变换和GM-ARMA组合模型的股指预测[J].智能系统学报,2011,6(3):279-282 Wu Zhaoyang.Using wavelet transformation and a GM-ARMA model to forecast stock index[J].CAAI Transactions on Intelligent Systems,2011,6(3):279-282(in Chinese)
[16]	李持佳,焦文玲,赵林波.燃气短期负荷预测的小波分析综合模型[J].天然气工业,2007,27(8):103 Li Chijia,Jiao Wenling,Zhao Linbo.A synthesis wavelet analysis method for short-term gas load prediction[J].Natural Gas Industry,2007,27(8):103(in Chinese)