Data-driven effectiveness evaluation modeling and simulation of anti-missile equipment system

Aiming at the problems that traditional effectiveness evaluation methods can not reflect the evolution, emergence and adaptability of the anti-missile equipment system, a data-driven effectiveness evaluation method of anti-missile equipment system was proposed. Based on the analysis of the characteristics of anti-missile equipment system and the shortage of traditional effectiveness evaluation method. the Bayes optimization algorithm was used to optimize the convolutional neural network hyperparameters, and the efficiency evaluation model of Bayes-CNN (Bayes convolutional neural network) was constructed. The flow and steps of Bayes-CNN system effectiveness evaluation algorithm were studied, and a set of completed efficiency evaluation algorithm was formed. Designed and validated the simulation experiment, input a lot of test data to Bayes-CNN model for training and learning, so as to obtain the simulation prediction of the effectiveness of anti-missile equipment system. The experimental results show that the error between the actual and expected output is very small, and the non-linear fitting effect is great so that it had a high degree of feasibility and reliability.