Evaluation of Combat Effectiveness of airborne Mission System Based on RBF Neural Network

In response to the evaluation of airborne mission systems' combat effectiveness, this article establishes a combat effectiveness evaluation index system and introduces the RBF neural network model to accomplish quantitative assessments of a specific aircraft mission system's combat performance. The simulation results indicate that the predicted combat effectiveness values align closely with the actual values, with a root mean square error (RMSE) of merely 0.00033, demonstrating precise quantitative evaluation of combat capabilities. This article employs the RBF neural network approach for combat effectiveness evaluation, ensuring the accuracy and applicability of the assessment for a specific aircraft mission system, thereby laying a solid foundation for maximizing its combat effectiveness.