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ThinPlateSpline_MatlabCode
说明： 一个薄板样条模型（thin plate spline）matlab源代码(A thin plate spline model (thin plate spline) matlab source code)
- 2008-09-17 18:31:18下载
- 积分：1
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location
用于三点定位的算法，是做声音定位可用的，希望有部分帮助，可适当运用(It is used to location the point you want.)
- 2020-09-10 13:08:00下载
- 积分：1
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SHUZHIFENXI
牛顿迭代法，用于求解非线性方程，具体使用方法见程序内部说明。(Newton iteration for solving nonlinear equations, the specific use of the procedure see the internal note.)
- 2007-11-05 09:01:46下载
- 积分：1
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elasticity
说明： abaqus vumat 子程序入门
UMAT FOR ABAQUS/STANDARD INCORPORATING ELASTIC BEHAVIOUR FOR PLANE STRAIN AND AXI-SYMMETRIC ELEMENTS.(introduction to abaqus vumat subroutine)
- 2020-07-08 19:20:08下载
- 积分：1
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(Calculus-.7ed)-.James.Stewart
世界上最好的微积分教材，搞程序的，研究的，数学的都应该了解，这个是James.Stewart的Calculus第七版，英文原版，彩色的，值得珍藏。(The world' s best calculus textbook, engage in programs, research, mathematics should understand that this is James.Stewart seventh edition of Calculus, English original, colorful and worth treasuring.)
- 2013-08-09 14:32:08下载
- 积分：1
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Allocation-problem
用计算机编程的方法建立试卷的合理分配问题的优化模型(Optimization model to establish the papers a reasonable allocation problem with computer programming methods)
- 2013-03-14 21:05:15下载
- 积分：1
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3
说明： 数值分析 显示算法 非线弹性 滞回曲线(Numerical analysis showed that the algorithm the linear elastic hysteresis curve)
- 2012-01-09 16:26:03下载
- 积分：1
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vc
说明： 这是高斯列主元消去法的完整程序，可以求解线性方程组(This is the Gaussian elimination method PCA out a complete program, you can solve the linear equations)
- 2014-10-25 18:45:15下载
- 积分：1
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主成分分析
说明： 利用python进行因子分析,因子分析可以进行降维!(Using Python factor analysis, factor analysis can reduce dimensionality!)
- 2020-09-18 08:57:54下载
- 积分：1
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SVD
％ 奇异值分解 (sigular value decomposition,SVD) 是另一种正交矩阵分解法；SVD是最可靠的分解法，
％ 但是它比QR 分解法要花上近十倍的计算时间。[U,S,V]=svd(A)，其中U和V代表二个相互正交矩阵，
％ 而S代表一对角矩阵。 和QR分解法相同者， 原矩阵A不必为正方矩阵。
％ 使用SVD分解法的用途是解最小平方误差法和数据压缩。用svd分解法解线性方程组，在Quke2中就用这个来计算图形信息，性能相当的好。在计算线性方程组时，一些不能分解的矩阵或者严重病态矩阵的线性方程都能很好的得到解( Singular value decomposition (sigular value decomposition, SVD) is another orthogonal matrix decomposition method SVD decomposition is the most reliable method, but it takes more than QR decomposition near ten times the computing time. [U, S, V] = svd (A), in which U and V on behalf of two mutually orthogonal matrix, and the S on behalf of a diagonal matrix. And QR decomposition are the same, the original matrix A is no need for the square matrix. The use of SVD decomposition method are used as a solution of least squares error method and data compression. Using SVD decomposition solution of linear equations, in Quke2 on to use this information to calculate the graphics performance quite good. In the calculation of linear equations, some indecomposable matrix or serious pathological matrix of linear equations can be a very good solution)
- 2020-12-21 10:29:08下载
- 积分：1
