Abstract:To increase the speed of calculating the Radar Cross Section(RCS) in the optimization design process for reducing the RCS of electrically large targets, a multi-layer Fully Connected Neural Network(FCNN) is trained using the results of models calculated by electromagnetic simulation software when employing heuristic algorithms for low-RCS optimization design of electrically large targets. During the optimization process, once the number of calculated models is sufficient to complete the training of the neural network, the trained neural network is employed to improve electromagnetic simulation calculations. Leveraging the faster computational speed of neural networks compared to electromagnetic simulations, the optimization design speed for low-RCS of electrically large targets is enhanced. Under the conditions of the electrically large target model selected in this paper and the optimization design using the simulated annealing method, the use of a multi-layer fully connected neural network to improve electromagnetic simulation calculations significantly increases the speed of low-RCS optimization design, reducing the required time from over 300 h to approximately 140 h.