径流混沌网络模型及其在长江三峡预测研究中的应用  

Research on the Forecast of Runoff by Chaotic Neural Networks

作　　者：万星[1,2] 唐天国[3] 

机构地区：[1]重庆交通大学机电学院,重庆400074 [2]重庆交通大学河海学院,重庆400074 [3]四川大学建环学院,成都610065

出　　处：《中国农村水利水电》2009年第4期1-3,6,共4页China Rural Water and Hydropower

基　　金：国家自然科学基金重点资助项目(50579022);重庆市科委自然科学基金:(CSTC;2007BB6431)

摘　　要：流域径流受诸多因素影响,变化复杂,仅凭观测站统计数据难以发现其演变规律。以混沌理论为基础,将三峡寸滩站月平均径流量时序曲线进行相空间重构,并确定合理饱和关联维数,再与神经网络结合,用多维相空间建立网络学习样本和教师值,参照饱和维数建立网络结构,形成混沌神经网络分析模型,以期发现流域径流模型的变化规律。结果表明,对三峡流域径流数据的混沌网络分解后,能找到其合理的演变规律,对三峡库区水资源的合理开发利用具有实际工程意义。Runoff is often affected by various factors and its character is complex and multivariate. Evolution regularity of runoff cannot be found only by using hydrographieal data from observing stations. Based on chaotic theories, phase space reconstruction had been completed by mean runoff from the Cuntan Station in the Three Gorges. Saturated association dimension is obtained. Learning and teaching values of Neural Networks can be achieved from multi-dimensional phase space of chaotic theories. Buildup of networks is built according to saturated association dimensions. So, the chaotic network model has been finished for runoff forecast. The results show that rea- sonable evolution regularity has been found from hydrographical data from the Cuntan Station by using the chaotic network method. These conclusions may serve as a reference to exploitation and utilization of hydrographical resources in the Three Gorges.

关 键 词：径流序列 混沌理论 人工神经网络 预测 

分 类 号：TV213.9[水利工程—水文学及水资源]