基于遗传神经网络的装备抢修性预测模型

摘要

The equipment's combat resilience is an inherent property dating from designing, and has a direct effection on BDRA. The model of combat resilience is a very important in its research, but there is much difficulty. BP, which is capable of learning by itself, can be used to simulate and forecast the combat resilience and the GA is utilized in order to improve BP's ability, then the equipment's combat resilience forecasting model based on GA and BP is built. The simulation result at last suggests that this model can simulate and forecast the combat resilience well, and it would be a good reference to the designation of combat resilience of new equipment.%装备的抢修性是装备设计时赋予装备的一种固有属性,直接影响到战场抢修工作.抢修性模型是抢修性研究的重要内容.针对抢修性量化模型难度大的特点,尝试采用具有自学习能力的神经网络对抢修性进行仿真预测,同时为提高神经网络的性能,运用遗传算法对其优化,从而得到基于遗传神经网络的装备抢修性预测模型.仿真结果表明,该模型可较好地用于抢修性的仿真预测,能为新研制装备的抢修性设计提供有益参考.