利用二次移动平均模型，引入均方拟合误差最小的原理确定出时段数n，对国民经济总收入、人口数量等项目进行了预测。
The total income of national economy and the amount of population are predicted with second-degree moving model and with the principle of least mean square fit error determining designated value n.
新算法使用时变遗忘因子对误差进行指数加权平均来估计均方误差，并使用该因子改变自适应迭代过程中滤波器系数向量的更新方向。
In the new algorithm, a time-variant forgetting factor is introduced to estimate the Mean Square Er-ror (MSE) and change the updating direction of adaptive filter coefficient vector.
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。
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。
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.
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