基于遗传算法和BP神经网络的武器系统安全评价模型

Cheng Kai, Zhang Hong-jun, Xuebing Bo, Shan Li-li
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引用次数: 0

摘要

传统的神经网络不可避免地存在局部极值问题,可能导致训练失败。在量化武器系统安全指标的基础上,采用基于改进遗传算法的神经网络建立了武器系统安全评价模型。利用改进的遗传算法对神经网络权值进行优化,并通过对神经网络的两次训练得到最终的评价值。仿真结果表明,混合算法收敛速度快,能有效避免局部极值问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Weapon systematic safety evaluation model based on genetic algorithm and BP neural network
The traditional neural network is unavoidable to present local extreme value question, may result in failing training. On the basis of quantization of weapon system safe index, it has adopted neural network based on improved genetic algorithm to set up the systematic safety evaluation model of the weapon. It utilizes improved genetic algorithm to optimize the weight of neural network and get the final assessment value through twice training of neural network. The simulation result implies that the convergence speed of hybrid algorithm is quick and it can avoid local extreme value question effectively.
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