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基于相空间重构与RBF神经网络的干旱预测模型
引用本文:侯姗姗,王鹏新,田苗.基于相空间重构与RBF神经网络的干旱预测模型[J].干旱地区农业研究,2011,29(1):224-230.
作者姓名:侯姗姗  王鹏新  田苗
作者单位:中国农业大学信息与电气工程学院;
基金项目:国家自然科学基金项目(40871159,40571111,40371083); 国家高技术研究发展计划课题(2007AA12Z139); 欧盟FP7项目(CallFP7-ENV-2007-1212921)
摘    要:通过引入混沌和相空间重构理论,将一维时间序列重构为多维序列,并与径向基函数神经网络模型相结合,建立了基于条件植被温度指数(VTCI)的干旱预测模型,并对其进行了验证.结果表明:干旱预测结果与实际干旱监测结果中的VTCI最大值、最小值、平均值及标准差十分接近,所有样本点的预测值的相对误差的绝对值均低于9%.经过a=0.0...

关 键 词:预测  条件植被温度指数  相空间重构  径向基函数神经网络

Application of phase space reconstruction and RBF neural network model in drought forecasting
HOU Shanshan,WANG Pengxin,TIAN Miao.Application of phase space reconstruction and RBF neural network model in drought forecasting[J].Agricultural Research in the Arid Areas,2011,29(1):224-230.
Authors:HOU Shanshan  WANG Pengxin  TIAN Miao
Institution:HOU Shan-shan,WANG Peng-xin,TIAN Miao(College of Information and Electrical Engineering,China Agricultural University,Beijing 00083,China)
Abstract:The chaos theories and phase reconstruction are introduced into drought forecasting,and the vegetation temperature condition index(VTCI) time series are expended to multivariate time series.Based on multi-dimension VTCI time series,phase reconstruction and neural network model are combined to establish the drought forecasting model.The results show that the minimum,maximum,average and standard deviation between predicted value and measured value are very close,all the sample sites predictive values of the r...
Keywords:forecasting  vegetation temperature condition index  phase reconstruction  RBF neural network  
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