A Multi-model Modeling Method Based on Supervised Affinity Propagation Clustering Algorithm

Abstract

Abstract: The traditional multimodel modeling method has big error because it does not consider the output error of the model during the clustering process, A multi-model modeling method based on supervised affinity propagation clustering algorithm was proposed. The principle is that the initial clusters are first obtained by the affinity propagation clustering algorithm, and then the clusters are adjusted cycledly in accordance with the output errors until the clusters do not change. Finally, the accurate clusters are got, and the sub-models are respectively built by least squares support vector machine so as to estimate the output. The method is used for the softsensor model to estimate the content of acetone at the outlet of a reaction vessel in a bisphenol A production process. The simulation results show that the method can get better modeling results than the traditional multi-model modeling method.

Key words: multi-models, supervised, affinity propagation clustering, least squares support vector machine (LSSVM)

CLC Number:

TP273

DENG Wei-Wei-1, YANG Hui-Zhong-1, 2 . A Multi-model Modeling Method Based on Supervised Affinity Propagation Clustering Algorithm[J]. Journal of Shanghai Jiaotong University, 2011, 45(08): 1172-1175.

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