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遗传算法与神经网络结合优化焊接接头力学性能预测模型

Optimization of mechanical properties prediction models of welded joints combined neural network with genetic algorithm

  • 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。

     

    Abstract: Genetic algorithm was used to optimize the backpropagation 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

     

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