基于小波消噪和混沌序列的SVM模型研究应用

Application of Chaotic Time Series and Wavelet De-noising to SVM Model and Its Application

基于小波消噪理论,采用改进小数据量法计算最大Lyapunov指数,对北碚水文站月径流时间序列进行混沌特性识别,利用C-C法重构相空间挖掘北碚站月径流时间序列中的信息,通过SCE-UA算法优化出惩罚因子、核宽度,并引入径向基核函数简化非线性问题的求解过程。实例结果表明,SVM径流预测模型实现了精度与实用性的统一,可较好处理复杂的水文序列,具有较高的泛化能力和预测精度,为资料匮乏地区预报研究提供了一种新方法。

Base on the wavelet de-nosing theory,chaotic characteristics of the monthly runoff series in Beibei hydrologic station is analyzed and an improved small data method is applied to calculate the largest Lyapunov index.The phase space is reconstructed by C-C method so that the information of runoff is investigated.Penalty factors and kernel width are optimized with SCE-UA algorithm and RBF kernel function is used to simplify solution procedure of non-liner problem.The results show that the unification of preci...