利用在线向量机和Unscented Kalman滤波进行降水序列滤波研究

Precipitation Series Filter Based on Online SVM and Unscented Kalman Filter

  • 摘要: 针对短周期降水序列模型估计困难、滤波误差不确定问题提出了在线向量机与Unscented Kalman滤波相结合的降水时间序列预测与滤波方法。从理论推导到真实数据的实验以及详细的误差分析证明了本方法对短周期降水序列滤波有较好的合理性和有效性。相比传统Kalman滤波方法和向量机滤波方法,该方法有更好的滤波性能和实用性。

     

    Abstract: In order to filter the series,the traditional algorithms require an explicit system model which is a difficult problem in nonlinear-system.We present an effective method based on the online support vector machine and Unscented Kalman filter method to filter a precipitation series.The experimental results show that our proposed method is more efficient to get accurate result than the traditional Kalman filter method and the support vector machine method.

     

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