基于遗传算法进化神经网络的潜射导弹筒盖压力预测

Pressure Forecasting of Submarine-Borne Missile Cover System Based on Neural Network Evolved by Genetic Algorithm

  • 摘要: 提出一种基于遗传算法进化神经网络的潜射导弹筒盖最大压力和压力持续脉宽的预测方法,遗传算法可依据个体适应度完全自适应,并在此基础上提出了一种提高遗传算法初始进化速度的随机基因偏移方法.训练结果表明,优化神经网络所形成的虚拟函数具有较好的遍历性,能够较好地反映样本的内在联系,借助该方法预测的潜射导弹筒盖压力特性具有较高的精度,可快速、准确地预测发射筒筒盖的压力特征.

     

    Abstract: A pressure forecasting method of submarine-borne missile cover system based on neural network evolved by genetic algorithm is proposed.Genetic algorithm is made completely self-adaptive depending on individual fitness.In addition,a new stochastic gene offset operation is used that improves the initial evolving velocity of the genetic algorithm.Training results showed that dummy function formed from optimum neural network has a good adaptability and can reflect the internal relation of samples.Using this method,pressure of submarine-borne missile cover system has been forecasted accurately.So the method can be used to forecast the pressure characteristics of submarine-borne missile cover system fastly and accurately.

     

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