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为了提高固体推进剂燃速预示精度,将遗传算法(Genetic Algorithm)与误差反传(Back Propagation)网络结构模型相结合,设计了用遗传算法优化神经网络权重的新方法。以固体推进剂燃速数据库为基础,对推进剂的燃速进行了预估,并与BP算法进行了比较。结果显示,预估值与实际值接近,误差小于BP算法模型,具有良好的预示效果,为推进剂燃速预估提供了新方法。
In order to improve the predictive accuracy of the combustion rate of solid propellants, a genetic algorithm is used to optimize the weights of neural networks by combining genetic algorithm with backpropagation network structure model. Based on the solid propellant burning rate database, the burning rate of the propellant was estimated and compared with the BP algorithm. The results show that the estimated value is close to the actual value, the error is less than the BP algorithm model, which has a good predictive effect and provides a new method for estimating the burning rate of propellant.