Using linear regressive models (e. g. AR, ARMA model) to fit and predict the climatic time series, the results are not sufficiently good because there exist nonlinear variations in the time series.
在用AR、ARMA等线性模式对气候序列进行拟合和预报时，由于气候序列中存在着非线性变化，所以拟合和预报效果往往不太理想。
The mathematical model of the continuous cooling transformation kinetics was obtained by linear regressive analysis.
用线性回归分析法得到动力学转变的数学模型。
Simulation results showed that the ANN model gave better predictions than the regressive model. The average relative error of ANN was 14.9% and that of linear regression was 25.8%.
模拟的结果显示ANN模型比线性回归模型有更好的预测能力，预测的平均相对误差：ANN模型为14.9%，线性回归模型为25.8%。
Simulation results showed that the ANN model gave better predictions than the regressive model. The average relative error of ANN was 14.9% and that of linear regression was 25.8%.
模拟的结果显示ANN模型比线性回归模型有更好的预测能力，预测的平均相对误差：ANN模型为14.9%，线性回归模型为25.8%。
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