[author_cn_name].[cn_title][J].空军工程大学学报:自然科学版,[year_id],[volume]([issue]):[start_page]-[end_page] 一种基于SVR的综合预测方法及应用-New Synthetic Prediction Method Based on SVR and Its Application
New Synthetic Prediction Method Based on SVR and Its Application
中文关键词: SVR  多元回归  主成分分析  飞机故障率  综合预测
英文关键词: support vector regression (SVR)  multivariate regression  principal component analysis  aircraft's failure ratio  synthetic prediction
张云龙,潘泉,张洪才 1.西北工业大学自动化学院陕西西安7100722.空军第一航空学院基础部河南信阳464000 
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      For the problem that the dependent variable has many independent variables and their sampling periods are also different, a predicting method is proposed by using synthetically the data analysis methods of support vector regression (SVR), multivariate regression and principal component analysis, etc. The method can be briefly described as follows: 1. Predicting with the independent variables which have dense sampling periods based on SVR, and then the results are synchronized to have the same sampling period with the dependent variable. 2. Amending the results by using another linear or non-linear method which includes SVR itself, with the rest independent variables which have the same sampling periods with the dependent variable. 3. In order to increase the predictive accuracy, three data processing methods (principal component analysis, standardization and normalization) are integrated. 4. Two approaches, error mean square line and small error probability, are also introduced to evaluating this synthetic method. By using the method, the mathematical relation between the aircraft's failure ratio and its anfractuous factors is first established. The results show that the method is efficient in predicting the aircraft's failure ratio. In the process of quantifying some influencing factors of the aircraft's failure ratio, the Pearson's correlation coefficient method is also adopted.
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