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Application of Least Squares Support Vector Machine Model in Bam Monitoring
Pages: 99-100,103
Year: Issue:  11
Journal: Yellow River

Keyword:  state space reconstructionleast squares support vector machine modeldam displacement;
Abstract: It put forward a kind of chaotic time series using least square support vector machine prediction method,which was based on phase space reconstruction and least squares support vector machine model (LS_SVM). It also described the steps of the least square algorithm for chaotic time series based on support vector machine model and pointed out that the evaluation index of the model for the mean absolute error (MAE)and the mean square error of prediction (PMSE). It predicted the vertical displacement of No. 102 measuring point in No. 5 dam section of a concrete dam by using the model. The results show that LS_SVM model of performance prediction of chaotic time series based is better. It can well reflect the model’s practical application ability,the fitting and prediction results of the model can meet the precision requirement,compared with the regres-sion model have high precision of prediction and can provide a new solution for dam monitoring.
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