神经网络在共混物改性中的应用

采用神经网络研究了PP/POE共混物的冲击性能,通过温度、配比与冲击强度的正交试验设计得出的数据来验证神经网络的效果。将10℃、20℃和30℃的实验数据用于学习训练,将0℃的实验数据用于预测。同时通过调节不同的参数得出三种不同结构神经网络模型分别进行预测。通过测试表明,神经网络对于共混改性的预测具有较好的效果。将神经网络方法用于聚合物共混改性的数据分析,能明显减少实验次数,提高实验效率,快速完成共混材料的产品配方设计,并得出了该网络类型下的模型参数调控的大致方向,表明对于小样本数据,较少的隐藏层与神经单元数量能取得更好的预测效果。

Application of Neural Network in Copolymer Modification

The properties of PP/POE blends were studied by neural network.The effect of neural network was verified by the data of orthogonal test design of temperature,proportion and impact strength.The experimental data of 10℃,20℃and 30℃are used for learning and training,and the experimental data of 0℃are used for testing.At the same time,three kinds of neural network models with different structures are obtained by adjusting different parameters.The results show that neural network has a good effect on the prediction of blending modification.Using neural network method to analyze the data of polymer blending modification can reduce the times of experiments,improve the efficiency of experiments,and quickly complete the product formulation design of blends.The general direction of model parameter adjustment under the network type is obtained,which shows that under the condition of less sample data,less number of hidden layers and neural units can achieve better prediction effect.