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- chaotic runoff time series 混沌径流时间序列
- In this paper, the wavelet multiple time scales analysis method was used to analyze annual runoff time series from 1882 to 2006 of Yichang Hydrological Station. 运用小波分析的多分辨率功能，对宜昌水文站1882-2006年年径流量时间序列资料进行了多时间尺度分析。
- Through analyzing problem of wavelet analysis,the annual runoff time series from 1956 to 2000 of Hejin hydrological station in Fen River are decomposed into multiple time-scale series with EMD method. 针对小波分析的不足之处,运用EMD法对汾河河津水文站还原后的天然径流量时序进行多时间尺度分析,并揭示了各分量的趋势变化及影响因素。
- characteristics of runoff time series 径流时序特征
- Application of Nonparametric Disaggregation Model for Stochastic Simulation of Daily Runoff Time Series in Flood Period 非参数解集模型在汛期日径流随机模拟中的应用
- Scale Invariant Analysis and Runoff Trend Prediction of the Runoff Time Series in the Yellow River 黄河径流序列标度不变性分析及趋势预测研究
- Hybrid Stochastic Model Base on Wavelet Analysis and Its Application in Prediction of Runoff Time Series 基于小波分析的组合随机模型及其在径流预测中的应用
- Research on Period of Runoff Time Series in the Upper Reach of the Yellow River Based on Wavelet Analysis 基于小波分析的黄河上游径流变化周期研究
- runoff time series 径流序列
- annual runoff time series 年径流时间序列
- monthly runoff time series 月径流时间序列
- Simulations show these two methods are effective for chaotic time series prediction,including ... 仿真表明,两种模型均能有效预报舰船摇荡极短期运动。
- On the basis of chaotic time series,this paper uses the BP neural network method to forecast the power load,and analyzes the model characteristic. 在混沌时间序列的基础上,应用BP神经网络对电力负荷进行了预测,并对模型特性进行了分析。
- This is called a deseasonalizing time series. 这叫调和时间数列。
- Time Series Analysis Hamilton J.D. 时间序列分析。
- Predicts the future values for a time series. 预测一个时序的未来值。
- Application of wavelet transform to monthly runoff time serial analysis in Zagunao watershed,the upper Minjiang River. 小波变换在岷江上游杂古脑流域径流时间序列分析中的应用
- As a new type of recurrent neural network,echo state network(ESN) is applied to nonlinear system identification and chaotic time series prediction. ESN(回声状态网络)是一种新型的递归神经网络;可有效处理非线性系统辨识以及混沌时间序列预测问题.
- Results A new mode of time series is established. 结果建立了一个新的时间序列模型。
- Simulations show that RBF networks models have good fitness and high accuracy of single and multistep prediction to the chaotic time series. 仿真结果表明,RBF网络模型对混沌时间序列有比较强的拟合能力和比较高的一步及多步预测精度。
