A Military Maneuver Engineering Support Evaluation Model Based on ANN and Super-Efficiency DEA

In this paper, we proposed a two-stage hybrid model combined back-propagation (BP) neural networks and super-efficiency data envelopment analysis (DEA) model. It was applied to evaluate the performance of military maneuver engineering support. The indices of military maneuver engineering support were firstly built. And the CCR model was switched to super- efficiency issue. It will solve the disadvantage of BP neural networks that the teacher scores were too subjective. The super-efficiency scores will help to discern the efficient decision making units as well. A numerical example illustrates the application of the suggested model. The BP super-efficiency DEA model has better forecasting precision and network error. It shows that the hybrid model we designed is available and efficient in the assessment of military maneuver engineering support.