High performance evaluation of seeker measurement based on improved BP neural network

The performance evaluation of weapon equipment runs through the whole life process of equipment development, which is important to equipment finalization and practical application. In view of that the traditional back propagation (BP) neural network is prone to local optimal and weapon equipment evaluation data are a small sample type, this paper proposes an improved BP neural network model based on data envelopment ana-lysis and the Bootstrap method. Three optimized parameters are obtained by data envelopment analysis, and expanded by the Bootstrap method, and then an evaluation model is established by the BP neural network. The experimental results show that the determination coefficient and error coefficient of the systemic aperture radar seeker are improved obviously. This model not only avoids the problems of strong subjectivity and low accuracy of the fuzzy comprehensive evaluation method, but also solves two shortcomings of the traditional BP neural network model which is prone to be involved in local optimization and the lack of weapon equipment data. © 2019, Editorial Office of Systems Engineering and Electronics. All right reserved.