Combat effectiveness evaluation method of photoelectric defense system based on BP neural network optimized by bat algorithm

The combat effectiveness is an important indicator of the photoelectric defense system quality. When used in combat, the influence factors of the combat effectiveness are complex and show a nonlinear relationship. This paper proposes to apply the BP neural network to combat effectiveness evaluation, propose the thought of make the photoelectric defense system combat effectiveness value to different “classification”. For the problem of the structure of BP neural network is difficult to determine, this paper puts forward to optimize the BP neural network weights and threshold by the bat algorithm, which gets the best weights and threshold of BP neural network. The example verifies the validity of the methods above, which overcome the weakness of the expert decision-making system not easy to modify and the poor quality of the adaptive ability.