Research on Aircraft State Prediction Method Based on SSA-TSVR
In order to build ground flight safety situation monitoring systems,solve the problem that abnormal data transmission occurs during the transmission process of aircraft status data to the ground,resulting in the inability to the real-time monitoring of air-craft status data,an aircraft status prediction method based on squirrel search algorithm and twin support vector regression(SSA-TS-VR)was proposed.the random forest method was used to analyze the feature importance of real flight data,screen and predict the important parameters closely related to the aircraft's state,and obtain the importance relationship between the parameters to be pre-dicted and the flight data.The twin support vector regression algorithm was used to build the prediction model,and predict the miss-ing key flight state parameters.The twin support vector regression model was optimized by using the SSA,and the optimal kernel function was selected according to different prediction objects,improving the prediction accuracy of the model.With flight altitude and speed as prediction objects,the prediction model realizes the accurate prediction of aircraft state by using incomplete flight data,which is of great significance for aircraft flight state monitoring.
aircraft state predictionTSVRSSArandom forestQAR data
万方数据
维普
