摘要
将混沌理论和神经网络相结合,建立了径流预报的混沌神经网络模型.利用混沌理论的相空间重构技术计算饱和嵌入维数,将其作为神经网络的输入层神经元个数;根据模型预测步长确定输出层神经元个数.对黄河干流三门峡站的日流量时间序列进行了模拟和预报,取到了较好的预报效果,为河川径流的预报工作提供了新方法.
The chaos theory and artificial neural networks （ANNs） are combined to establish a chaotic ANNs model for runoff forecast. The chaos theory＇s phase space reconstruction is used to calculate saturated embedding dimensions which are served as the number of input layer neurons of ANNs. Then the number of output layer neurons is determined according to prediction step size of the model. The daily discharge time series of Sanmenxia station in the main stream of Yellow River is simulated and forecasted, from which the satisfaetory results are obtained. The research provides a new way for rivers runoff forecast.
出处
《华北水利水电学院学报》
2012年第4期19-21,共3页
North China Institute of Water Conservancy and Hydroelectric Power
基金
国家自然基金重大项目(51190093/E0901)
河南省高等学校青年骨干教师资助项目(2009GGJS-061)
河南省教育厅自然科学研究资助计划项目(2009A570002
2011A570012)
关键词
水文学
径流预报
混沌
神经网络
hydrology
runoff forecast
chaos theory
artificial neural networks （ANNs）
