基于相空间重构和Volterra的非线性寒区气温预测方法
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摘要
对寒区冬季气温时间序列的混沌特性及其应用技术进行了研究.先通过0-1混沌测试法确定寒区冬季气温时间序列具有混沌特性,然后通过相空间重构,分别利用C-C算法和G-P算法确定延迟时间和嵌入维数.在此基础上,提出了一种相空间重构和Volterra滤波的寒区冬季气温预测方法.实例分析表明,提出的预测方法在预测精度、预测误差、预测效果方面均优于常见模型,证明该预测方法是可行和有效的.
The chaos characteristics of temperature time series in cold regions' winter were studied. Firstly, it is determined by 0-1 time series of chaotic test method that the chaos characteristics of temperature in cold regions' winter. Secondly, reconstructing phase spaces, and using the C-C algorithm and G-P algorithm to determine the delay time and embedding dimension. Then, based on phase space reconstruction and Vinterra filter, a temperature prediction method in cold regions' winter is proposed. Case analysis shows that the proposed method is superior to the common model in accuracy, prediction error and effect, and the prediction method is feasible and effective.
The chaos characteristics of temperature time series in cold regions' winter were studied. Firstly, it is determined by 0-1 time series of chaotic test method that the chaos characteristics of temperature in cold regions' winter. Secondly, reconstructing phase spaces, and using the C-C algorithm and G-P algorithm to determine the delay time and embedding dimension. Then, based on phase space reconstruction and Vinterra filter, a temperature prediction method in cold regions' winter is proposed. Case analysis shows that the proposed method is superior to the common model in accuracy, prediction error and effect, and the prediction method is feasible and effective.
引文
1肖建民,金龙海,谢永刚,等.寒区水库冰盖形成与消融机理分析.水利学报,2004,6:80-85
2乔继平,王富强,代俊峰.黄河宁蒙河段封开河日期预报方法研究.人民黄河,2013,35:6-7
3黄文峰,李志军,贾青,等.水库冰表层形变的现场观测与分析.水利学报,2016,12:1585-1592
4贾丽伟,李维京,陈德亮,等.东北地区月平均大气环流型与哈尔滨气候关系的初步研究.气象学报,2006,64:236-245
5金秀良,宋燕,吴洪,等.应用北半球大气环流系统预测中国降水量和气温.气象科技,2014,42:1028-1038
6凌志明,王毅刚,陶玲君.基于OGRE的实时光照及降雨天气仿真.杭州电子科技大学学报(自然科学版),2015,35:31-35
7丛凌博,蔡吉花.ARMA模型在哈尔滨气温预测中的应用.数学的实践与认识,2012,42:190-195
8蔡吉花,张世军,杨丽.EMD-SVM在南京市月平均气温预测中的应用.数学的实践与认识,2014,22:103-111
9冀鸿兰,朝伦巴根,陈守煜.模糊优选神经网络BP模型在黄河内蒙段封开河预报中的应用.水利水电科技进展,2008,28:70-72
10金志凤,符国槐,黄海静,等.基于BP神经网络的杨梅大棚内气温预测模型研究.中国农业气象,2011,3:362-367
11周华任,李浩然,孙学金,等.一种基于季节指数的灰色马尔科夫气温预测模型.数学的实践与认识,2016,4:167-173
12罗妤,吴坚,徐鑫晶.合肥市平均气温动态变化的非平稳时序分析.安徽农业大学学报,2013,40:1059-1062
13唐舟进,任峰,彭涛,等.基于迭代误差补偿的混沌时间序列最小二乘支持向量机预测算法.物理学报,2014,63:78-87
14 Gottwald G A,Melbourne I.A new test for chaos in deterministic systems.Proc R Soc A-Math Phys Eng Sci,2004,460:603-611
15 Gottwald G A,Melbourne I.On the implementation of the 0-1 test for chaos.SIAM J Appl Dyn Syst,2009,8:129-145
16李新杰,胡铁松,郭旭宁,等.0-1测试方法的径流时间序列混沌特性应用.水科学进展,2012,6:861-868
17 Takens F.Detecting strange attractors in turbulence.In:Rand D,Young LS,Eds.Dynamical Systems and Turbulence,Warwick 1980.Lecture Notes in Mathematics,Vol 898.Berlin,Heidelberg:Springer,1981.366-381
18汪树玉,刘国华,杜王盖,等.大坝观测数据序列中的混沌现象.水利学报,1999,7:23-27
19 Grassberger P,Procaccia I.Measuring the strangeness of strange attractors.Physica D,1983,9:189-208
20 Kim H S,Eykholt R,Salas J D.Nonlinear dynamics,delay times,and embedding windows.Physica D,1999,127:48-60
21吕金虎,陆君安,陈士华.混沌时间序列分析及其应用.武汉:武汉大学出版社,2005.237
22 Wolf A,Swift J B,Swinney H L,et al.Determining Lyapunov exponents from a time series.Physica D,1985,16:285-317
23 H.Kashani M,Ghorbani M A,Dinpashoh Y,et al.Integration of Volterra model with artificial neural networks for rainfall-runoff simulation in forested catchment of northern Iran.J Hydrol,2016,540:340-354
24 Zhang B,Billings S A.Volterra series truncation and kernel estimation of nonlinear systems in the frequency domain.Mech Syst Signal Pr,2017,84:39-57
25白建东,叶德谦,李春兴.混沌时间序列的Volterra级数多步预测研究.计算机仿真,2008,25:274-276
26贺静,赵众,董叶伟.基于Volterra模型的预测控制及应用.控制理论与应用,2015,32:312-319
27孟洋洋,卢继平,王坚,等.基于Volterra自适应滤波器的风电功率混沌预测.电力系统保护与控制,2012,40:90-95
28南敬昌,刘元安,李新春,等.记忆效应非线性功放扩展Volterra模型分析与构建.电子与信息学报,2008,30:2021-2024
2乔继平,王富强,代俊峰.黄河宁蒙河段封开河日期预报方法研究.人民黄河,2013,35:6-7
3黄文峰,李志军,贾青,等.水库冰表层形变的现场观测与分析.水利学报,2016,12:1585-1592
4贾丽伟,李维京,陈德亮,等.东北地区月平均大气环流型与哈尔滨气候关系的初步研究.气象学报,2006,64:236-245
5金秀良,宋燕,吴洪,等.应用北半球大气环流系统预测中国降水量和气温.气象科技,2014,42:1028-1038
6凌志明,王毅刚,陶玲君.基于OGRE的实时光照及降雨天气仿真.杭州电子科技大学学报(自然科学版),2015,35:31-35
7丛凌博,蔡吉花.ARMA模型在哈尔滨气温预测中的应用.数学的实践与认识,2012,42:190-195
8蔡吉花,张世军,杨丽.EMD-SVM在南京市月平均气温预测中的应用.数学的实践与认识,2014,22:103-111
9冀鸿兰,朝伦巴根,陈守煜.模糊优选神经网络BP模型在黄河内蒙段封开河预报中的应用.水利水电科技进展,2008,28:70-72
10金志凤,符国槐,黄海静,等.基于BP神经网络的杨梅大棚内气温预测模型研究.中国农业气象,2011,3:362-367
11周华任,李浩然,孙学金,等.一种基于季节指数的灰色马尔科夫气温预测模型.数学的实践与认识,2016,4:167-173
12罗妤,吴坚,徐鑫晶.合肥市平均气温动态变化的非平稳时序分析.安徽农业大学学报,2013,40:1059-1062
13唐舟进,任峰,彭涛,等.基于迭代误差补偿的混沌时间序列最小二乘支持向量机预测算法.物理学报,2014,63:78-87
14 Gottwald G A,Melbourne I.A new test for chaos in deterministic systems.Proc R Soc A-Math Phys Eng Sci,2004,460:603-611
15 Gottwald G A,Melbourne I.On the implementation of the 0-1 test for chaos.SIAM J Appl Dyn Syst,2009,8:129-145
16李新杰,胡铁松,郭旭宁,等.0-1测试方法的径流时间序列混沌特性应用.水科学进展,2012,6:861-868
17 Takens F.Detecting strange attractors in turbulence.In:Rand D,Young LS,Eds.Dynamical Systems and Turbulence,Warwick 1980.Lecture Notes in Mathematics,Vol 898.Berlin,Heidelberg:Springer,1981.366-381
18汪树玉,刘国华,杜王盖,等.大坝观测数据序列中的混沌现象.水利学报,1999,7:23-27
19 Grassberger P,Procaccia I.Measuring the strangeness of strange attractors.Physica D,1983,9:189-208
20 Kim H S,Eykholt R,Salas J D.Nonlinear dynamics,delay times,and embedding windows.Physica D,1999,127:48-60
21吕金虎,陆君安,陈士华.混沌时间序列分析及其应用.武汉:武汉大学出版社,2005.237
22 Wolf A,Swift J B,Swinney H L,et al.Determining Lyapunov exponents from a time series.Physica D,1985,16:285-317
23 H.Kashani M,Ghorbani M A,Dinpashoh Y,et al.Integration of Volterra model with artificial neural networks for rainfall-runoff simulation in forested catchment of northern Iran.J Hydrol,2016,540:340-354
24 Zhang B,Billings S A.Volterra series truncation and kernel estimation of nonlinear systems in the frequency domain.Mech Syst Signal Pr,2017,84:39-57
25白建东,叶德谦,李春兴.混沌时间序列的Volterra级数多步预测研究.计算机仿真,2008,25:274-276
26贺静,赵众,董叶伟.基于Volterra模型的预测控制及应用.控制理论与应用,2015,32:312-319
27孟洋洋,卢继平,王坚,等.基于Volterra自适应滤波器的风电功率混沌预测.电力系统保护与控制,2012,40:90-95
28南敬昌,刘元安,李新春,等.记忆效应非线性功放扩展Volterra模型分析与构建.电子与信息学报,2008,30:2021-2024