中文摘要：

分析了卡尔曼滤波算法的基本原理及其对空气质量指数（AQI）的预测机制。利用自回归滑动平均模型（ARMA）为卡尔曼滤波建立数学模型,提出了将径向基函数（RBF）神经网络融合于卡尔曼滤波,实现了新的融合算法对AQI进行预测。根据AQI时间序列的特点,建立了自回归预测模型,进而建立卡尔曼滤波的状态方程和测量方程。采用随机梯度逼近训练算法训练RBF神经网络,用RBF神经网络的输出作为卡尔曼滤波测量方程的观测值。仿真结果表明,融合了RBF神经网络后的卡尔曼滤波预测算法改善了单一方法预测滞后的现象,减小了误差,提高了预测精度。

英文摘要：

The basic principle of the Kalman filtering algorithm and its prediction mechanism of air quality index（AQI）were analyzed.This paper using auto-regressive and moving average model（ARMA）to establish a mathematical model for Kalman filtering and put forward radial basis function（RBF）neural network merging with Kalman filtering to achieve a new fusion algorithm for AQI forecast.According to the characteristics of AQI time series,auto-regressive prediction model was established,then Kalman filtering state equation and measurement equation were established.The stochastic gradient approximation algorithm was used to train the RBF neural network,and the output of RBF neural network was used as the observation value of the Kalman filtering measurement equation.Simulation results showed that compared with a single method,the Kalman filtering prediction algorithm combined with RBF neural network had improved the lag phenomenon,reduced errors and raised the prediction accuracy.