In order to deal with the nonlinear and complexity of Aromatics isomerization process

multi-model algorithm with affinity propagation(AP)cluster and least squares support vector machines is proposed to make up the shortcoming of single model.Firstly

the data of isomerization process are clustered into several groups with AP algorithm.Then a sub-model is constructed for each group with LS-SVM. By comparing Euclidean distances between the test sample and all cluster centers

the group which the test sample belongs to is determined.Finally

the test sample is input into the corresponding sub-model to predict the output.Experimental results show higher accuracy of the proposed algorithm，compared with single model and k-means clustering based neural networks.