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《Computers and Applied Chemistry》 2012-02
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Prediction of montmorillonite suspensions zeta-potential based on support vector machine

Tao Huaizhi,Pu Xiaolin*,Wang Gui,Pan Lijuan and Xiang Chaogang (State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation,Southwest Petroleum University,Chengdu 610500, Sichuan China)  
Three models were used to research four kinds of agents(PEG,SDBS,PS and HPAM) effecting on zeta-potential of montmorillonite suspensions,which were based on multiple linear regression(MLR),artificial neural network(ANN) and support vector machine(SVM).The methods of neural network ensemble and parameter optimization were used to improve the generalization ability of models of ANN and SVM in the training. The results indicate that the parameter optimization SVM model is the most accurate model.Its average error rate is 3.88%and maximum error rate is 7.55%.
【Fund】: 国家自然科学基金资助项目(50774065)
【CateGory Index】: O611.3;TP18
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