Air Target Threat Assessment Based on Improved Moth Flame Optimization-Gray Neural Network Model

Abstract

Air target threat assessment is a key issue in air defense operations. Aiming at the shortcomings of traditional threat assessment methods, such as one-sided, subjective, and low-accuracy, a new method of air target threat assessment based on gray neural network model (GNNM) optimized by improved moth flame optimization (IMFO) algorithm is proposed. The model fully combines with excellent optimization performance of IMFO with powerful learning performance of GNNM. Finally, the model is trained and evaluated using the target threat database data. The simulation results show that compared with the GNNM model and the MFO-GNNM model, the proposed model has a mean square error of only 0.0012 when conducting threat assessment, which has higher accuracy and evaluates 25 groups of targets in 10 milliseconds, which meets real-time requirements. Therefore, the model can be effectively used for air target threat assessment.