基于BP和Hopfield神经网络的航空肼燃料保障安全评价
发布时间：2018-07-25 19:43
【摘要】：针对航空肼燃料保障安全评价的复杂性和非线性,提出并建立了基于BP和Hopfield神经网络的动态安全评价模型。在综合分析国内外肼燃料保障安全评价的基础上,针对航空肼燃料保障过程中出现的问题,构建并优化了指标体系,选取前馈神经网络中的BP网络和反馈神经网络中的Hopfield网络建立评价模型。在详细说明了BP和Hopfield神经网络的构建方法后,进行实例验证,并对预测效果进行了比较分析。仿真表明,两种模型都能正确评价安全保障状态。但在收敛速度、联想记忆功能方面Hopfield神经网络优于BP神经网络。将BP和Hopfield神经网络用于肼燃料保障安全评价过程中,具有适用性和可行性,对于航空肼燃料保障的安全建设与安全管理研究具有重要意义。
[Abstract]:In view of the complexity and nonlinearity of aviation hydrazine fuel safety evaluation, a dynamic safety evaluation model based on BP and Hopfield neural network is proposed and established. On the basis of synthetically analyzing the safety evaluation of hydrazine fuel support at home and abroad, and aiming at the problems in the process of aviation hydrazine fuel support, the index system is constructed and optimized. BP network in feedforward neural network and Hopfield network in feedback neural network are selected to establish evaluation model. The construction method of BP and Hopfield neural networks is explained in detail, and the results of prediction are compared and analyzed. Simulation results show that both models can correctly evaluate the security state. But Hopfield neural network is superior to BP neural network in convergence speed and associative memory function. The application of BP and Hopfield neural networks to the safety evaluation of hydrazine fuel support is applicable and feasible, which is of great significance to the safety construction and safety management of aviation hydrazine fuel support.
【作者单位】： 空军勤务学院;
【分类号】：TP183;V312
，
本文编号：2144873
[Abstract]:In view of the complexity and nonlinearity of aviation hydrazine fuel safety evaluation, a dynamic safety evaluation model based on BP and Hopfield neural network is proposed and established. On the basis of synthetically analyzing the safety evaluation of hydrazine fuel support at home and abroad, and aiming at the problems in the process of aviation hydrazine fuel support, the index system is constructed and optimized. BP network in feedforward neural network and Hopfield network in feedback neural network are selected to establish evaluation model. The construction method of BP and Hopfield neural networks is explained in detail, and the results of prediction are compared and analyzed. Simulation results show that both models can correctly evaluate the security state. But Hopfield neural network is superior to BP neural network in convergence speed and associative memory function. The application of BP and Hopfield neural networks to the safety evaluation of hydrazine fuel support is applicable and feasible, which is of great significance to the safety construction and safety management of aviation hydrazine fuel support.
【作者单位】： 空军勤务学院;
【分类号】：TP183;V312
，
本文编号：2144873
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