关键词: 自动气象站, 间断噪声, 日平均气温, 平方平均隶属度函数, 模糊支持向量机, 补偿

Abstract:

To ensure the integrity and accuracy of the meteorological data, combined with automatic weather station's daily average temperature data which contained discontinuous noise, three types of membership functions were submitted. A compensation algorithm of Fuzzy Support Vector Machine (FSVM) based on root-mean-square membership function was designed and the compensation model was established too. Finally, the FSVM method was compared with the traditional Support Vector Machine (SVM) method. The experimental results show that the proposed algorithm has good recognition capability for noise points. After interpolation, the data precision was 1.4℃, better than 1.6℃ of the traditional SVM method. Moreover, the whole data precision was 1.13℃, superior to 1.42℃ of the traditional SVM method.

Key words: automatic weather station, discontinuous noise, daily average temperature, root-mean-square membership function, Fuzzy Support Vector Machine (FSVM), compensation