摘要
为了能利用工艺参数实时预测药柱密度并提高密度预测精度,提出采用改进遗传算法优化BP网络(improved genetic algorithm backpropagation neural network,IGA-BPNN)的炸药密度预测模型。通过动态调整GA的交叉概率和变异概率,确定BPNN权重和阈值的最优值,构建IGA-BP预测模型,利用采集的工艺参数,基于所构建模型进行炸药密度预测。实验结果表明:改进的GA对交叉率和变异率做出了更好的调整,能快速搜寻BPNN的最优权重和阈值,提高炸药压制密度的预测精度。
In order to predict the density of explosive column in real time and improve the prediction accuracy,an improved genetic algorithm was used to optimize the BP network(improved genetic algorithm backpropagation neural network,IGA-BPNN)model for predicting explosive density.By dynamically adjusting the crossover probability and mutation probability of GA,the optimal values of BPNN weights and thresholds were determined,and the IGA-BP prediction model was constructed to predict the explosive density based on the collected process parameters.The experimental results show that the improved GA makes a better adjustment to the crossover rate and mutation rate,can quickly search the optimal weight and threshold of BPNN,and improve the prediction accuracy of explosive pressing density.
作者
史慧芳
郭进勇
伍凌川
杨治林
袁申
李全俊
王勇
黄荔
Shi Huifang;GuoJinyong;Wu Lingchuan;Yang Zhilin;Yuan Shen;Li Quanjun;Wang Yong;Huang Li(Department of Intelligent Manufacture,Automation Research Institute Co.,Ltd.of China South Industries Group Corporation,Mianyang 621000,China)
出处
《兵工自动化》
北大核心
2024年第11期76-82,86,共8页
Ordnance Industry Automation
关键词
炸药密度
改进遗传算法
交叉率
变异率
BP神经网络
explosive density
improved genetic algorithm
crossover rate
mutation rate
BP neural network
作者简介
第一作者:史慧芳(1984-),男,河南人,硕士。
