Search - k matrix

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[matlab] disp_rand DL : 0: 本程序用matlab生成白噪声，并且基于一个离散线性随机系统的模型生成了y(k)和x(k)，绘制出了x(k|k-1)和x(k)的对比曲线，求出了提前一步预报的误差协方差阵的稳定值-the procedures used Matlab generate white noise, and on a discrete linear stochastic systems model generated y (k) and x (k), mapping out the x (k | k-1) and x (k) contrast curves, get a step ahead forecasting error covariance matrix of stable value
Date : 2025-12-29 Size : 1kb User : 孙磊
[matlab] sdir2cas DL : 0: s平面中直接形式到级联形式的转换％适合模拟滤波器的％C为增益系数％B为包含各bk的K乘3维实系数矩阵％A为包含各ak的K乘3维实系数矩阵％b为直接形式的分子多项式系数％a为直接形式的分母多项式系数-s-plane in the direct form and cascade form of analog filters suitable for conversion of C for the gain coefficient B to include the bk of the K x 3-dimensional real coefficient matrix A to include all of K by ak real 3-dimensional coefficient matrix b for direct forms of molecular polynomial coefficients a direct form of the denominator polynomial coefficients
Date : 2025-12-29 Size : 1kb User : 吴江华
[matlab] dir2cas DL : 0: 直接型到级联型的形式转换％ [b0,B,A]=dir2cas(b,a) ％b 为直接型的分子多项式系数％a 为直接型的分母多项式系数％b0为增益系数％B 为包含各bk的K乘3维实系数矩阵％A 为包含各ak的K乘3维实系数矩阵％-directly to the cascade-type conversion in the form of%% [belts, B, A] = dir2cas (b, a)% b-to direct the molecular polynomial coefficients% for a direct-denominator polynomial coefficients% of the gain coefficient belts% B To contain the bk K by the three-dimensional real coefficient matrix% A to include all ak K by the three-dimensional real coefficient matrix%
Date : 2025-12-29 Size : 1kb User : 吴江华
[matlab] 231226 DL : 0: 空间后方交汇求解相机外方位元素，变量如下％ x，y 控制点像点坐标％ X，Y，Z 控制点空间坐标％f焦距％X0，Y0，Z0，a，b，c六个外方位元素％x0，y0，-f内方位元素：光心坐标％cha，chb，chc：外方位角元素改正数％count 记录迭代次数％R 旋转矩阵％A 线性化的偏导系数矩阵％L 常数项矩阵％M0 外方位元素矩阵％M1 外方位元素改正数矩阵-meeting space for rear camera position outside elements, as follows% variable x, y control point pixel coordinates% X, Y, Z coordinates control room focal length f%% X0, Y0, Z0, a, b, c 6 exterior orientation elements% x0, y0,- f position within elements : Optical Center coordinates% cha, chb, chc : Foreign elements azimuth correction% record count the number of iterations rotation matrix R%% A linear partial derivative of the coefficient matrix% L constant Matrix% M0 Orientation% M1 matrix elements of exterior orientation correction matrix
Date : 2025-12-29 Size : 1kb User : 王立钊
[matlab] power_standardization DL : 0: 幂法是一种计算矩阵主特征值（矩阵按模最大的特征值）及对应特征向量的迭代方法，特别适用于大型稀疏矩阵。但是，一般幂法迭代向量v的各个不等于零的分量将随k 趋向于无穷大而使计算机溢出。因此，我们必须对某通幕法进行规范。即规范化幂法 -Power Method is a calculation of the main eigenvalue matrix (matrix according to the largest eigenvalue modulus) and the corresponding eigenvector of the iterative method, especially for large sparse matrix. However, the general power-law iteration vector v is not equal to zero all the components will be as k tends to infinity overflow of the computer. Therefore, we must pass a law to regulate screen. That is, standardized Power Method
Date : 2025-12-29 Size : 12kb User : knight
[matlab] simulation DL : 0: 对一个50个结点（更多的节点的网络只需要修改模块中的标量维数就行）的复杂非线性耦合网络进行同步化仿真。首先生成K矩阵，然后运行simulink，即可得到50个洛仑兹混沌节点复杂网络的同步化曲线。-Of a 50-node (more network nodes only need to modify module scalar dimension on the line) the complexity of nonlinear coupling network synchronization simulation. First Generation K matrix, and then run the simulink, can be chaotic 50 Lorentz complex network node synchronization curve.
Date : 2025-12-29 Size : 9kb User : zhongsir
[matlab] FCM DL : 0: Initialize U=[uij] matrix, U(0) At k-step: calculate the centers vectors C(k)=[cj] with U(k) Update U(k) , U(k+1) If || U(k+1) - U(k)||< then STOP otherwise return to step 2. - Initialize U=[uij] matrix, U(0) At k-step: calculate the centers vectors C(k)=[cj] with U(k) Update U(k) , U(k+1) If || U(k+1)- U(k)||< then STOP otherwise return to step 2.
Date : 2025-12-29 Size : 382kb User : 魏嘉
[matlab] linear_system DL : 0: 一个自动控制专业的实用程序。以一个四阶系统为例，对任意的参数矩阵研究其反馈镇定性，通过因式分解法，得到所有能镇定G的一般形式的K值，用matlab进行了仿真。-Automatic control of a professional utility. A fourth-order system as an example, the parameters of any study the feedback stabilization of the matrix, and through the factorization method, by all G to calm the general forms of K values, using matlab simulation.
Date : 2025-12-29 Size : 2kb User : eestarliu
[matlab] m12_3 DL : 0: 为了改善噪声e(k)为有色噪声模型的系统参数估计的统计特性，提出了一种增广矩阵的方法，称为增广最小二乘算法，MATLAB实现范例-In order to improve the noise e (k) for the colored noise model of the system parameters estimated statistical characteristics, an Augmented Matrix method, called Augmented Least Squares, MATLAB implementation examples
Date : 2025-12-29 Size : 1kb User : 冷强
[matlab] FGPMBEAM2 DL : 0: skyline method to inverse K matrix in [K]{X}={F}
Date : 2025-12-29 Size : 4kb User : amin
[matlab] kmeanmatlab DL : 0: k-mean program INPUT A Matrix or dataset K Number of clusters to find (optional default: 2) MAXIT maximum number of iterations (optional default: 50) INIT Labels for initialisation, or rand : take at random K objects as initial means, or kcentres : use KCENTRES for initialisation (default) FID File ID to write progress to (default [], see PRPROGRESS) -k-mean program INPUT A Matrix or dataset K Number of clusters to find (optional default: 2) MAXIT maximum number of iterations (optional default: 50) INIT Labels for initialisation, or rand : take at random K objects as initial means, or kcentres : use KCENTRES for initialisation (default) FID File ID to write progress to (default [], see PRPROGRESS)
Date : 2025-12-29 Size : 10kb User : letian
[matlab] art DL : 0: 用于解反问题的代数重建法，对于Ax=b，输入矩阵A，列向量b，以及迭代步数k，可求的列向量x-Algebraic solution of the inverse problem for the reconstruction of France, for Ax = b, the input matrix A, the column vector b, as well as the number of iterations k, rectifiable column vector x
Date : 2025-12-29 Size : 1kb User : gongwei
[matlab] SVM DL : 0: In this paper, we show how support vector machine (SVM) can be employed as a powerful tool for $k$-nearest neighbor (kNN) classifier. A novel multi-class dimensionality reduction approach, Discriminant Analysis via Support Vectors (SVDA), is introduced by using the SVM. The kernel mapping idea is used to derive the non-linear version, Kernel Discriminant via Support Vectors (SVKD). In SVDA, only support vectors are involved to obtain the transformation matrix. Thus, the computational complexity can be greatly reduced for kernel based feature extraction. Experiments carried out on several standard databases show a clear improvement on LDA-based recognition
Date : 2025-12-29 Size : 2kb User : sofi
[matlab] k-means DL : 0: 名为k-means的MATLAB函数，实现k均值算法。输入矩阵X,w，输出最终估计值和聚类的标识数字。-Called the k-means of the MATLAB function, to achieve k means algorithm. Input matrix X, w, the output value of the final estimates and cluster identification number.
Date : 2025-12-29 Size : 1kb User : menghang
[matlab] kmeans DL : 0: function [L,C] = kmeans(X,k) KMEANS Cluster multivariate data using the k-means++ algorithm. [L,C] = kmeans(X,k) produces a 1-by-size(X,2) vector L with one class label per column in X and a size(X,1)-by-k matrix C containing the centers corresponding to each class. Version: 07/08/11 Authors: Laurent Sorber (Laurent.Sorber@cs.kuleuven.be) References: [1] J. B. MacQueen, "Some Methods for Classification and Analysis of MultiVariate Observations", in Proc. of the fifth Berkeley Symposium on Mathematical Statistics and Probability, L. M. L. Cam and J. Neyman, eds., vol. 1, UC Press, 1967, pp. 281-297. [2] D. Arthur and S. Vassilvitskii, "k-means++: The Advantages of Careful Seeding", Technical Report 2006-13, Stanford InfoLab, 2006. -function [L,C] = kmeans(X,k) KMEANS Cluster multivariate data using the k-means++ algorithm. [L,C] = kmeans(X,k) produces a 1-by-size(X,2) vector L with one class label per column in X and a size(X,1)-by-k matrix C containing the centers corresponding to each class. Version: 07/08/11 Authors: Laurent Sorber (Laurent.Sorber@cs.kuleuven.be) References: [1] J. B. MacQueen, "Some Methods for Classification and Analysis of MultiVariate Observations", in Proc. of the fifth Berkeley Symposium on Mathematical Statistics and Probability, L. M. L. Cam and J. Neyman, eds., vol. 1, UC Press, 1967, pp. 281-297. [2] D. Arthur and S. Vassilvitskii, "k-means++: The Advantages of Careful Seeding", Technical Report 2006-13, Stanford InfoLab, 2006.
Date : 2025-12-29 Size : 1kb User : ehsan
[matlab] K-medoids-with-the-analysis- DL : 0: 基于聚类的K中心点算法，附带说明文档，代码简单高效，很好的利用了矩阵的代数运算。数学思想较为高深，但通过仔细研读说明文档和动手操作，matlab数学分析能力可以得到有效的提高-K medoids clustering annotated document, the code is simple and efficient, good use of matrix algebra operations. Mathematical thinking is more profound, but by carefully studying the documentation and hands-on the Matlab mathematical analysis ability can be effective to improve
Date : 2025-12-29 Size : 10kb User : 菜包
[matlab] [emuch.net]K-means DL : 0: 简易k-means聚类算法实例，针对二维数据，自带简易数据矩阵，分析结果有输出图像。-Simple k-means clustering algorithm example, for two-dimensional data, comes with simple data matrix, analysis results output image.
Date : 2025-12-29 Size : 2kb User : zl
[matlab] get_modal.m DL : 0: 根据C、M、K矩阵计算模态参数：固有频率、振型向量、阻尼比，并可区分比例阻尼还是非比例阻尼-Calculate the modal parameters C, M, K matrix: natural frequency, mode shape vectors, damping ratio, and can distinguish proportional damping or non-proportional damping
Date : 2025-12-29 Size : 1kb User : Yuhao
[matlab] computervision_20151235_seohyeonShin DL : 0: camera calibration matlab code i attached image file and code this can calculate A matrix, K matrix, P matrix. but you must check and insert image points set by your hand. open image-> click and confirm (x,y). and insert mat( , ) this it input data.
Date : 2025-12-29 Size : 2.84mb User : seo
[matlab] pathanddistance DL : 0: 任意两点间最短路算法 Warshall-Floyd算法思想，最短距离矩阵+任意给定两顶点的最短路所包含顶点。(The shortest path algorithm between any two points The idea of Warshall-Floyd algorithm, the shortest distance matrix + the shortest path contained by any given two vertex is the vertex.)
Date : 2025-12-29 Size : 2kb User : SARAH寒

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