一种新的基于误差矢量化的选择性神经网络集成方法及其在高密度聚乙烯串级反应过程中的应用(英文)

Chemical processes are complex, for which traditional neural network models usually can not lead to satisfactory accuracy. Selective neural network ensemble is an effective way to enhance the generalization accuracy of networks, but there are some problems, e.g., lacking of unified definition of diversity among component neural networks and difficult to improve the accuracy by selecting if the diversities of available networks are small. In this study, the output errors of networks are vectorized, the diversity of networks is defined based on the error vectors, and the size of ensemble is analyzed. Then an error vectorization based selective neural network ensemble (EVSNE) is proposed, in which the error vector of each network can offset that of the other networks by training the component networks orderly. Thus the component networks have large diversity. Experiments and comparisons over standard data sets and actual chemical process data set for production of high-density polyethylene demonstrate that EVSNE performs better in generalization ability.

A New Selective Neural Network Ensemble Method Based on Error Vectorization and Its Application in High-density Polyethylene (HDPE) Cascade Reaction Process

Chemical processes are complex,for which traditional neural network models usually can not lead to satisfactory accuracy.Selective neural network ensemble is an effective way to enhance the generalization accuracy of networks,but there are some problems,e.g.,lacking of unified definition of diversity among component neural networks and difficult to improve the accuracy by selecting if the diversities of available networks are small.In this study,the output errors of networks are vectorized,the diversity of networks is defined based on the error vectors,and the size of ensemble is analyzed.Then an error vectorization based selective neural network ensemble (EVSNE) is proposed,in which the error vector of each network can offset that of the other networks by training the component networks orderly.Thus the component networks have large diversity.Experiments and comparisons over standard data sets and actual chemical process data set for production of high-density polyethylene demonstrate that EVSNE performs better in generalization ability.