芳烃异构化过程的多模型建模

Multi-modeling of aromatics isomerization process

针对芳烃异构化过程的非线性、复杂性,提出利用仿射传播聚类和最小二乘支持向量机对芳烃异构化过程进行多模型建模,以此来弥补单一模型建模的不足。首先仿射传播聚类对异构化数据聚类,利用最小二乘支持向量机对聚类之后的各个类分别建立子模型,通过计算欧氏距离来判断测试样本的所属类,将测试样本送入所属类的模型进行预测,以此来实现异构化过程的多模型预测。实验证明,与单模型以及基于k均值聚类的神经网络模型相比,本文提出的基于仿射传播聚类的最小二乘支持向量机模型更能准确地预测输出。

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.