摘要
基于建立的反向传播(back propagation,BP)神经网络焊接接头力学性能预测模型,并综合运用遗传算法(genetic algorithm,GA)来优化BP神经网络连接权的方法,对模型预测性能进行了有效的改进,提高了神经网络模型的预测精度和泛化能力。对模型性能的分析表明,焊接接头力学性能预测模型的预测规律符合已有研究结论,预测误差小于5%。随着样本数据的不断充实,样本覆盖空间的不断扩大,力学性能预测模型的应用范围将不断扩大,其实际应用价值也必将越来越高。
Genetic algorithm was used to optimize the back-propagation neural network connection weights and improve the models predicted precision and generalization ability on the basic of the mechanical properties prediction models of welded joints established with back-propagation neural network.The performance analysis shows that the predicted trend agrees well with the previous research work and the predicted error is less than 5%.It is obvious that the models will be more applicable and valuable in the practice with the enlargement of database and the data-covering space.
出处
《焊接学报》
EI
CAS
CSCD
北大核心
2007年第12期69-72,共4页
Transactions of The China Welding Institution
关键词
遗传算法
神经网络
反向传播
力学性能预测模型
genetic algorithm
neural network
back propagation
mechanical properties prediction model
