The root mean square relative error, mean absolute relative error and maximize absolute relative error of SVM model generalization performance are 1.06%, 0.96% and 1.16%, respectively.

对SVM多元非线性回归泛化性能进行测试，其均方根相对误差为1.06%，平均绝对相对误差为0.96%，最大绝对相对误差为1.16%。

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Coefficient determination, absolute bias, relative absolute bias, root mean square error and relative root mean square error were employed to evaluate the precision of different model systems.

采用确定系数、绝对误差、相对绝对误差、均方根误差、相对均方根误差等模型评价指标对不同模型系统的精度进行比较分析。

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Results show that the RBFNN is obviously superior to the traditional linear model, and its MAE (mean absolute error) and RMSE (root mean square error) are 41.8 and 55.7, respectively.

结果显示，该模型预测效果明显优于传统的线性自回归预测模型，各月平均的平均绝对误差（MAE）和均方误差（RMSE）达到41.8和55.7。

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