基于人工神经网络和混合遗传算法的炸药爆速预测

运用基于最优保存和自适应交叉变异的混合遗传算法训练的BP神经网络,根据三维数据建模和炸药的分子量、氧平衡以及装药密度,构建了一个3-4-1型的炸药爆速预测BP神经网络模型。同时利用训练好的神经网络模型对炸药的爆速进行了预测。预测结果表明:模型预测值与有关文献的实验值接近,绝对误差为±7%;也说明了炸药的分子量,氧平衡和装药密度等相关参数与其爆速具有一定的可类推性。

Predicting the Detonating Velocity of Explosives Based on Artificial Neural Network and Hybrid Genetic Algorithm

The model predicting the detonation velocity of explosives was founded on the back propagation (BP) neural-network (BP neural-network has been trained by a hybrid genetic algorithm which based on elitist model algorithm and adaptive crossover mutation), the three-dimension data modeling, molecular weight, oxygen balance and charge density of explosives. The detonation velocity of some explosives were predicted by using the ameliorative BP neural network model. The forecast results indicate that the predicted values by using this model approaches the experimental volues in literature. The absolute errors are ±7%. And there are some analogies between the relative parameters (including the molecular, oxygen balance and charge density of explosives) and the detonation velocity of explosives. The results also show that the yield model has high predicting accuracy. It is a novel method for predicting and estimating the detonation velocity of new explosives.