基于BP神经网络的锁模机构运行振动量的预测

摘要

This paper established an operating parameters optimization model of bottle blowing machine clamping mechanism by using BP neural network, including the determination of network model structure, the training of the network model, the verification of network model. Finally predict the vibration quantity under the optimal operation parameters combination by using the established BP neural network. Through with the actual test results, obtain the optimal operation parameters of clamping mechanism. It can save resources, reduce the production cost. So it has a certain guidance in practical production.%利用BP神经网络建立吹瓶机锁模机构的运行参数的优化模型，通过BP神经网络模型结构的确定、BP神经网络模型的训练和BP神经网络模型的验证，预测出最优参数组合下锁模机构运行过程中的振动量，与现场实际测试结果进行对比，得到锁模机构的最优运行参数，节约资源，降低生产成本，指导实际生产。