High Temperature Storage Environment Prediction Technology of Airborne Equipment Combat Readiness Based on DPFNN Network
To overcome the difficulty of using thermodynamic equations to describe the relationship between the cabin environment and the external environment,and to accurately predict the extreme high temperature was proposed due to the complex heat transfer relationship during the airborne external combat readiness storage.A process neural network structure was established and a direct method of solving orthogonal transformation coefficients for discrete inputs was proposed.In order to accelerate the convergence speed,a gradient descent algorithm PALA was proposed to establish the temperature prediction model of air-to-air missile when it is hung up on the ground.Compare the generalization ability of DPFNN,ANN and MLRM.The temperature prediction model based on DPFNN can predict the temperature change in the cabin according to the change of environmental conditions for three consecutive days,and the maximum absolute error is only 1.17 ℃,which has good generalization ability.The process neural network temperature prediction model based on DPFNN has the ability to accurately predict the airborne external temperature,and the method can be used to predict the airborne external storage temperature,and provide reference for determining the environmental adaptability requirements of airborne external storage temperature.
万方数据
维普
