| 基于GM(0,N)和RBF的小样本时程数据预测 |
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| 引用本文： | 张诚,江琼.基于GM(0,N)和RBF的小样本时程数据预测[J].计算机工程与应用,2005,41(5):62-64,206. |
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| 作者姓名： | 张诚 江琼 |
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| 作者单位： | 深圳电视台技术管理中心制作部,深圳,518021;武汉理工大学计算机科学与技术学院计算机技术系,武汉,430070 |
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| 基金项目： | 国家“十五”科技攻关项目资助(项目号:2001BA307B01-02-01) |
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| 摘 要： | RBF网络具有良好的非线性函数逼近能力,且收敛速度快,而灰色GM(,)静态模型对小样本线性数据的预0N测精度高,将两者有机结合起来,提出了一种新的小样本数据预测方法,即灰色RBF(GRBF)静态预测法。同时,为了提高RBF网络的预测精度和运算效率,文中采用ROLS和后向选择法来训练网络。将GRBF静态预测方法应用到小样本时程数据的预测中,实验结果表明,此预测方法快捷简便,精度高,具有良好的实用性。
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| 关 键 词： | 灰色RBF算法 RBF神经网络 GM(0 N)静态模型 ROLS和后向选择算法 小样本时程数据 |
| 文章编号： | 1002-8331-(2005)05-0062-03 |
The Prediction. of Small Sample Time-displacement Data Based on GM(0,N) and RBF |
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| Zhang Cheng,Jiang Qiong.The Prediction. of Small Sample Time-displacement Data Based on GM(0,N) and RBF[J].Computer Engineering and Applications,2005,41(5):62-64,206. |
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| Authors: | Zhang Cheng Jiang Qiong |
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| Affiliation: | Zhang Cheng1 Jiang Qiong21 |
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| Abstract: | RBF(Radial Basis Function) network has good ability in approaching nonlinear function,and its convergent speed is rapid.But Grey Model(0,N) can make an accurate precision of small sample linear data.So,a new method on the basis of RBF and GM(0,N) is proposed in the paper.The method can estimate small sample data more accurately and it is called Grey RBF static prediction method,that is GRBF.In addition,the ROLS and the backward selection algorithm are adopted in the paper to improve precision of prediction.Using the method in practice,the result shows that the method is easy,convenient,accurate and good practicality. |
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| Keywords: | Grey RBF(GRBF) RBF neural network GM(0 N)static model ROLS and backward selection algorithm small sample time-sisplacement data |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
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