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《Advances of Power System & Hydroelectric Engineering》 2008-02
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Application of LSSVM to Seepage Monitoring of Dam

ZHANG Zhen-zhen,LI Zhi-lu,WANG Ke,LI Bo(Xi'an University of Technology,Xi'an 710048,China)  
Training slow of SVM restrict its development and application.Suykens presents a new method of support vector machine-least squares support vector machine.Least squares support vector machine is the development and improvement of support vector machines.Lssvm use equality constraints to alternative inequality constraints.So the speed of solution accelerates greatly.In this paper it is used in dam seepage monitoring and compared with the traditional support vector machine.The results show that the predicting effect of the two methods is good relatively.But the training efficient of least squares SVM is higher than support vector machine.
【CateGory Index】: TV698.12
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