| 混沌时间序列支持向量机模型及其在径流预测中应用 |
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| 引用本文： | 于国荣,夏自强.混沌时间序列支持向量机模型及其在径流预测中应用[J].水科学进展,2008,19(1):116-122. |
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| 作者姓名： | 于国荣 夏自强 |
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| 作者单位： | 1.河海大学水文水资源与水利工程科学国家重点实验室, 江苏, 南京, 210098; |
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| 摘 要： | 以重构相空间理论为基础,探讨了混沌时间序列的支持向量机预测模型建模的思路、特点及关键参数的选取。利用饱和关联维数法进行相空间重构,并运用改进小数据量法计算最大Lyapunov指数,对宜昌站月径流时间序列进行混沌特性识别。在运用混沌时间序列的支持向量机模型对月径流预测的应用中,引入了径向基核函数,简化了非线性问题的求解过程。实例表明,该模型能较好地处理复杂的水文序列,具有较高的泛化能力和很好的预测精度。
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| 关 键 词： | 混沌 相空间重构 水文时间序列 支持向量机 径向基核函数 径流预测 |
| 文章编号： | 1001-6791（2008）01-0116-07 |
| 收稿时间： | 2007-07-20 |
| 修稿时间： | 2007年7月20日 |
Prediction model of chaotic time series based on support vector machine and its application to runoff |
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| YU Guo-rong,XIA Zi-qiang.Prediction model of chaotic time series based on support vector machine and its application to runoff[J].Advances in Water Science,2008,19(1):116-122. |
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| Authors: | YU Guo-rong XIA Zi-qiang |
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| Affiliation: | 1.State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China;2.College of Water Resources and Environment, Hohai University, Nanjing 210098, China |
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| Abstract: | Chaos theory and support vector machine have great capability of dealing with nonlinear matter. Based on the phase-space reconstitution theory, the prediction model of chaos time series is built by using the support vector machine in this paper, the method, the characteristic, and the selecting of the key parameters in the modeling is discussed. Firstly the phase- space re-constitution is made by saturated correlation dimension, so that information of monthly runoff series is profoundly in- vestigated. At the same time, the maximum Lyapunov exponent is computed using the improved small-data method, and it is used to recognize the chaotic feature of the monthly runoff at YiChang. In the application of chaos time series using support vector machine model to predict the monthly runoff, the RBF kernel function is introduced, which simplified the course of solving non-linear problems. It is shown by the study case that the model proposed in the paper can process a complex hydro- logical data sieres better, and has better generalization and prediction accuracy. |
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| Keywords: | chaos phase space reconstruction hydrologic time series support vector machine RBF kernel function runoffforecast |
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