A Method for Generating Small Sample Data and Evaluating the Effectiveness of Combat Weapons Based on Conditional Adversarial Nets

A neural network has received attention in weapon performance evaluation, one-dimensional combat data in the standard sample generation method, poor sample feature extraction generation, and the network training process could be more convenient. This paper proposes a small sample data generation based on CGAN and weapon combat effectiveness evaluation method - CMGAN algorithm. Based on the advantages of conditional confrontation networks in image recognition, the sample data of one-dimensional combat weapons are projected into two-dimensional space. A two-dimensional convolution neural network is used to extract sample features. Then further optimize and screen the generated samples, and finally, use the generated virtual samples for GA-BP (genetic algorithm back-propagation) neural network effectiveness evaluation of combat weapons. The results show that the improved CMGAN algorithm can generate higher-quality data. It effectively improves the accuracy of combat weapon effectiveness assessment and network training speed and has good engineering application feasibility.