基于支持向量机的飞机重着陆预测模型  

A Prediction Model of Airplane Hard Landing Based on Support Vector Machine

作　　者：常文兵[1] 张佳宁[1] 周晟瀚[1] 

机构地区：[1]北京航空航天大学可靠性与系统工程学院,北京100191

出　　处：《飞机设计》2017年第2期19-22,共4页Aircraft Design

基　　金：自然科学基金资助项目(71501007);航空科学基金(2014ZG51075)

摘　　要：研究以建立一种重着陆预测模型为目的,筛选飞机着陆过程中部分飞参数据,有针对性地提出一套飞参数据处理与分析方法,基于处理后的飞参数据建立一种支持向量机的重着陆预测模型,通过特征选取与参数优化,对预测模型做进一步修正,可实现75.69%的重着陆预测率。预测模型可有效实现重着陆的预测,对飞机着陆安全预警工作,具有重要借鉴意义。The research aims to establish a prediction model for hard landing. Some flight parameters from aircraft landing process were selected to provide a set of processing and analysis method for flight parameters and establish a prediction model for hard landing using support vector machine based on the processed flight parameters. The prediction model was corrected by feature selection and parameters optimization that can realize a rate of 75.69% for hard landing prediction. It is extremely significant for early warning of aircraft landing safety that the prediction of hard landing can be real- ized by the model.

关 键 词：重着陆 飞参数据 支持向量机 预测模型 

分 类 号：N945.17[自然科学总论—系统科学]