1) global prediction
全局预测
1.
A new method of global prediction for chaotic time series based on continued fractions;
混沌时间序列全局预测新方法——连分式法
2) Global history prediction
全局历史预测
3) global neural networks forecasting
全局神经网络预测
1.
According to the nonlinear characteristics of sliver evenness,a local linear forecasting method and a global neural networks forecasting method were given to predict a segment of sliver evenness.
根据纱条不匀存在着的非线性、时变性和不确定作用关系,应用局域线性预测法和全局神经网络预测法进行了预测分析。
4) local prediction
局域预测
1.
The neighbor point selection method for local prediction based on information criterion;
基于信息准则的局域预测法邻近点的选取方法
2.
The weight-dynamic local prediction model is presented based on the reconstructed phase space, which takes the generalized degrees of freedom and neighbors′ weight into account, and the decisive condition of the op.
为了确定滞时、嵌入维数和最邻近点数这3个混沌时间序列局域预测模型参数,首先利用关联积分法确定滞时和嵌入维数,重构混沌时间序列的相空间;而后在此基础上,提出了一种新的预测模型——加权动态局域预测模型。
3.
The prediction method of weight local basis function is presented based on the deep research on local prediction for chaotic time series.
在深入研究混沌时间序列局域预测方法的基础上,提出了一种加权局域基函数预测方法。
补充资料：发育进度预测法(见发生期预测)
发育进度预测法(见发生期预测)
发育进度预测法见发生期预测。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条