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在大坝工程变形分析和预测方面,研究了一种基于支持向量度的模糊最小二乘支持向量机(LS-SVM)算法,结合具体实例进行对比分析,结果表明模糊LS-SVM模型的预测精度要高于LS-SVM模型,且支持向量机(SVM)的稀疏性也优于LS-SVM模型,可以很好地应用于大坝变形监测分析。
In the field of dam engineering deformation analysis and prediction, a support vector-based fuzzy least squares support vector machine (LS-SVM) algorithm is studied, and compared with the concrete examples. The results show that the prediction accuracy of the fuzzy LS-SVM model Which is higher than that of LS-SVM model. Moreover, the sparsity of support vector machine (SVM) is also better than LS-SVM model, which can be well applied to dam deformation monitoring and analysis.