提出一种基于最小二乘重构和WENO限制器的非结构网格高精度有限体积方法.用中心网格的某些邻居网格建立重构多项式,给出一定的原则搜索和存储足够多的邻居网格以建立重构多项式,采用最小二乘法求解重构多项式的系数.用一种通用的方法控...提出一种基于最小二乘重构和WENO限制器的非结构网格高精度有限体积方法.用中心网格的某些邻居网格建立重构多项式,给出一定的原则搜索和存储足够多的邻居网格以建立重构多项式,采用最小二乘法求解重构多项式的系数.用一种通用的方法控制重构邻居个数,以减少存储和计算,采用WENO限制器和旋转Riemann求解器以达到统一的高精度并且抑制守恒律方程求解中的非物理振荡.为检验上述算法,以基于节点的梯度重构,Bath and Jesperson限制器的二阶算法为基准,给出三阶和四阶格式与二阶格式以及高阶格式若干经典算例计算结果的对比和分析.展开更多
Least squares support vector machines (LS-SVMs) are modified support vector machines (SVMs) that involve equality constraints and work with a least squares cost function, which simplifies the optimization procedure. I...Least squares support vector machines (LS-SVMs) are modified support vector machines (SVMs) that involve equality constraints and work with a least squares cost function, which simplifies the optimization procedure. In this paper, a novel training algorithm based on total least squares (TLS) for an LS-SVM is presented and applied to multifunctional sensor signal reconstruction. For three different nonlinearities of a multifunctional sensor model, the reconstruction accuracies of input signals are 0.001 36%, 0.031 84% and 0.504 80%, respectively. The experimental results demonstrate the higher reliability and accuracy of the proposed method for multifunctional sensor signal reconstruction than the original LS-SVM training algorithm, and verify the feasibility and stability of the proposed method.展开更多
文摘提出一种基于最小二乘重构和WENO限制器的非结构网格高精度有限体积方法.用中心网格的某些邻居网格建立重构多项式,给出一定的原则搜索和存储足够多的邻居网格以建立重构多项式,采用最小二乘法求解重构多项式的系数.用一种通用的方法控制重构邻居个数,以减少存储和计算,采用WENO限制器和旋转Riemann求解器以达到统一的高精度并且抑制守恒律方程求解中的非物理振荡.为检验上述算法,以基于节点的梯度重构,Bath and Jesperson限制器的二阶算法为基准,给出三阶和四阶格式与二阶格式以及高阶格式若干经典算例计算结果的对比和分析.
基金the National Natural Science Foundation of China (Nos. 60772007 and 60672008)China Postdoctoral Sci-ence Foundation (No. 20070410258)
文摘Least squares support vector machines (LS-SVMs) are modified support vector machines (SVMs) that involve equality constraints and work with a least squares cost function, which simplifies the optimization procedure. In this paper, a novel training algorithm based on total least squares (TLS) for an LS-SVM is presented and applied to multifunctional sensor signal reconstruction. For three different nonlinearities of a multifunctional sensor model, the reconstruction accuracies of input signals are 0.001 36%, 0.031 84% and 0.504 80%, respectively. The experimental results demonstrate the higher reliability and accuracy of the proposed method for multifunctional sensor signal reconstruction than the original LS-SVM training algorithm, and verify the feasibility and stability of the proposed method.
