一种基于新型蚁群算法的聚丙烯熔融指数预报模型

Melt index prediction of polypropylene based on a new ant colony optimization

聚丙烯熔融指数的实时预报非常重要却十分困难,提出了一种经过新型蚁群算法优化后的PCA-RBF神经网络方法进行熔融指数预报。PCA将原始数据从高维空间映射到低维空间,剔除冗余信息和提取过程特征;RBF神经网络则用来拟合输入与输出之间的非线性关系;最后用适用于连续空间寻优问题的新型蚁群算法对RBF神经网络权值进行优化。实际生产数据的研究结果,表明了所提出的熔融指数预报模型的准确性和可靠性。

Melt index prediction of polypropylene is important but very difficult.A method based on PCA-RBF neural network which is optimized by a new ant colony system is proposed.Principal component analysis（PCA）is used to map high-dimension initial data to new low-dimension data,and then the corrections of the input variables are eliminated and the most relevant process features are selected.Radical basis function（RBF）neural network is used to characterize the nonlinearity.Finally the new ACS which works for continuous optimization problems is employed to optimize the weights of the RBF neural network.According to the research on the data from a real plant,it shows that the model works well and provides promising accuracy and reliability.