UCAV situation assessment based on SAE-ELM

Abstract A Stacked Autoencoder Extreme learning machine (SAE-ELM) assessment model is proposed to study the weight of an Unmanned Combat Air Vehicle (UCAV) situation assessment. On basis existing air combat description parameters and their optimized advantage function weights, mapping relationship between weights established by using SAE-ELM achieve a more accurate evaluation. From four error indicators MAE, RMSE, MAPE, R-Squared, simulation experiment shows that evaluation smallest can effectively