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.