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971.
972.
用矩阵运算实现曲线拟合中的最小二乘法 总被引:7,自引:0,他引:7
介绍了一种用矩阵运算实现曲线拟合过程中的最小二乘法的方法 ,避免复杂的求偏导过程 ,使得曲线拟合计算变得十分容易 ,在仪器仪表和传感器标定中有很好的应用价值。文章最后并有举例说明。 相似文献
973.
In this work, we consider the problem of solving , , where b
(k+1) = f(x
(k)). We show that when A is a full matrix and , where depends on the specific software and hardware setup, it is faster to solve for by explicitly evaluating the inverse matrix A
−1 rather than through the LU decomposition of A. We also show that the forward error is comparable in both methods, regardless of the condition number of A. 相似文献
974.
We consider the symmetric formulation of the interior penalty discontinuous Galerkin finite element method for the numerical
solution of the biharmonic equation with Dirichlet boundary conditions in a bounded polyhedral domain in
. For a shape-regular family of meshes consisting of parallelepipeds, we derive hp-version a priori bounds on the global error measured in the L2 norm and in broken Sobolev norms. Using these, we obtain hp-version bounds on the error in linear functionals of the solution. The bounds are optimal with respect to the mesh size h and suboptimal with respect to the degree of the piecewise polynomial approximation p. The theoretical results are confirmed by numerical experiments, and some practical applications in Poisson–Kirchhoff thin
plate theory are presented. 相似文献
975.
Shigeo Abe 《Pattern Analysis & Applications》2007,10(3):203-214
In this paper we discuss sparse least squares support vector machines (sparse LS SVMs) trained in the empirical feature space,
which is spanned by the mapped training data. First, we show that the kernel associated with the empirical feature space gives
the same value with that of the kernel associated with the feature space if one of the arguments of the kernels is mapped
into the empirical feature space by the mapping function associated with the feature space. Using this fact, we show that
training and testing of kernel-based methods can be done in the empirical feature space and that training of LS SVMs in the
empirical feature space results in solving a set of linear equations. We then derive the sparse LS SVMs restricting the linearly
independent training data in the empirical feature space by the Cholesky factorization. Support vectors correspond to the
selected training data and they do not change even if the value of the margin parameter is changed. Thus for linear kernels,
the number of support vectors is the number of input variables at most. By computer experiments we show that we can reduce
the number of support vectors without deteriorating the generalization ability.
Shigeo Abe received the B.S. degree in Electronics Engineering, the M.S. degree in Electrical Engineering, and the Dr. Eng. degree, all from Kyoto University, Kyoto, Japan in 1970, 1972, and 1984, respectively. After 25 years in the industry, he was appointed as full professor of Electrical Engineering, Kobe University in April 1997. He is now a professor of Graduate School of Science and Technology, Kobe University. His research interests include pattern classification and function approximation using neural networks, fuzzy systems, and support vector machines. He is the author of Neural Networks and Fuzzy Systems (Kluwer, 1996), Pattern Classification (Springer, 2001), and Support Vector Machines for Pattern Classification (Springer, 2005). Dr. Abe was awarded an outstanding paper prize from the Institute of Electrical Engineers of Japan in 1984 and 1995. He is a member of IEEE, INNS, and several Japanese Societies. 相似文献
Shigeo AbeEmail: |
Shigeo Abe received the B.S. degree in Electronics Engineering, the M.S. degree in Electrical Engineering, and the Dr. Eng. degree, all from Kyoto University, Kyoto, Japan in 1970, 1972, and 1984, respectively. After 25 years in the industry, he was appointed as full professor of Electrical Engineering, Kobe University in April 1997. He is now a professor of Graduate School of Science and Technology, Kobe University. His research interests include pattern classification and function approximation using neural networks, fuzzy systems, and support vector machines. He is the author of Neural Networks and Fuzzy Systems (Kluwer, 1996), Pattern Classification (Springer, 2001), and Support Vector Machines for Pattern Classification (Springer, 2005). Dr. Abe was awarded an outstanding paper prize from the Institute of Electrical Engineers of Japan in 1984 and 1995. He is a member of IEEE, INNS, and several Japanese Societies. 相似文献
976.
模糊时间Petri网的时间推理及其在过程监测中的应用 总被引:2,自引:0,他引:2
针对传统分析方法的不足,提出用线性逻辑给出模糊时间Petri网描述和时间推理的方法。该方法能清楚地分析模糊时间Petri网的运行行为,具体例子说明了其在系统过程监测和诊断中的应用。 相似文献
977.
基于解耦控制的同步传动技术的应用研究 总被引:3,自引:0,他引:3
对龙门移动式镗铣床同步传动不一致性问题进行研究,用两台直线电机作为龙门柱纵向进给的传动机构,分析了扰动与输出之间存在的耦合,设计出一种解耦控制器,并用龙门框架不平行时所产生的应力变化进行补偿。仿真结果表明,系统性能良好,并能快速实现同步运行。 相似文献
978.
979.
模式识别技术对Al_2O_3-TiC-ZrO_2纳米复合材料制备工艺参数的优化和预报 总被引:1,自引:1,他引:0
利用燃烧合成 热压工艺制备Al2 O3 TiC ZrO2 纳米复合材料 ,利用计算机模式识别技术中的非线性映照对其工艺参数进行分析。以陶瓷的抗弯强度和断裂韧性作为判据 ,给出参数目标优化区域。采用非线性逆映照研究不同工艺参数对材料性能的影响趋势 ,并对工艺参数进行预报和设计 ,再用实验结果予以验证 ,克服了传统“炒菜式”合成方法的盲目性。 相似文献
980.