人工神经网络在聚氨酯配方设计中的应用研究

Application of Artificial Neural Network in the Polyurethane Formulation Design

应用人工神经网络技术,采用Matlab软件设计BP网络模型,建立聚氨酯配方技术参数与拉伸强度的复杂的非线性关系,从而能够预测指定的聚氨酯配方技术参数所对应的拉伸强度值。结果证明,神经网络技术能够应用于聚氨酯配方设计,所建立的神经网络模型能较正确地反映聚氨酯配方技术参数与其拉伸强度之间的规律性。模型对拉伸强度的预测误差基本上可控制在4.60以内,对实验具有明显的指导作用。

Applying artificial neural network technology and using MATLAB software to design BP network model to establish the complex non- linear relationship between technical parameters of polyurethane formulations and tensile strength can predict the corresponding tensile strength value of the specified technical parameters of polyurethane formulations. The research results demonstrate that neural network technology can be used in the design of polyurethane formulation and the established neural network model can more or less correctly reflect the regularity between technical parameters of polyurethane and its tensile strength. Model prediction error for tensile strength can be controlled within 4. 60 which shows the model has a guiding significance in the experiment.