搜索资源列表
xianshipin
- 四个显示字符数据表放在50H-6FH单元内，字符用8*8点阵，R4（30H）用于 控制显示静止字的时间，R5（31H）静止字显示跳转地址步距，B内放显示首址-Table 4 shows data on the character 50H-6FH unit, character 8* 8 Dot Matrix, R4 (30H) for control shows the time the word static, R5 (31H) characters display a static
maseidel
- 用Gauss-Seidel迭代法解线性方程组Ax=b， A为系数矩阵，b为右端向量-Using Gauss-Seidel iteration method for solving linear equations Ax = b, A as the coefficient matrix, b is the right end of the vector
majacobi
- 用Jacobi迭代法解线性方程组Ax=b，A为系数矩阵，b为右端向量-Solution using Jacobi iterative method of linear equations Ax = b, A as the coefficient matrix, b is the right end of the vector
mexSparseLogical0Diag
- Because of memory constraints, it is often impossible to change by subscr ipt all the elements of a large sparse matrix to zero. This leads to changing the elements in a loop, which is horrendously slow. This mex solves that problem. Usage: B =
kalman_filter
- OPTIONAL INPUTS (string/value pairs [default in brackets]) model - model(t)=m means use params from model m at time t [ones(1,T) ] In this case, all the above matrices take an additional final dimension, i.e., A(:,:,m), C(:,:,m), Q(:,:,m), R
cgls
- 用于解反问题的共轭梯度法，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-Solution of inverse problems for the conjugate gradient method, for Ax = b, the input matrix A, the column vector b, as well as the number of iterations k, rectifiable column vector x
mr2
- 用于解反问题的算法，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-The algorithm for solution of the inverse problem, for Ax = b, the input matrix A, the column vector b, as well as the number of iterations k, rectifiable column vector x
nu
- 用于解反问题的算法，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-The algorithm for solution of the inverse problem, for Ax = b, the input matrix A, the column vector b, as well as the number of iterations k, rectifiable column vector x
LSQR
- 采用CG法求解稀疏不对称的Ax=b-Implementation of a conjugate-gradient type method for solving sparse linear equations and sparse least-squares problems: Solve Ax = b or minimize || Ax- b ||2 or minimize || Ax- b ||2+ d2 ||x||2. The matrix A may be squ
MINRES
- 采用CG法求解稀疏对称奇异矩阵得到的Ax=b-Implementation of a conjugate-gradient type method for solving sparse linear equations: Solve Ax = b or (A- sI)x = b. The matrix A- sI must be symmetric but it may be definite or indefinite or singular. The scalar s is a
SYMMLQ
- 采用CG法求解稀疏对称非奇异矩阵得到的线性系统Ax=b-Implementation of a conjugate-gradient type method for solving sparse linear equations: Solve Ax = b or (A- sI)x = b. The matrix A- sI must be symmetric and nonsingular, but it may be definite or indefinite. The scal
BRMUL
- 求m*n阶矩阵A与n*k阶矩阵B的乘积矩阵C=AB-we can get the product matrix C from m*n matrix A and n*k matrix B.
BCMUL
- 求m*n阶复矩阵A与n*k阶复矩阵B的乘积矩阵C=AB。-we can get product matrix C from m*n complex matrix A and n*k complex matrix B.
single
- 一般性的奇异值分解算法，float浮点型。-SGGSVD computes the generalized singular value decomposition (GSVD) * of an M-by-N real matrix A and P-by-N real matrix B: * * U*A*Q = D1*( 0 R ), V*B*Q = D2*( 0 R ) * * where U, V and Q are orthogonal matric
single
- 使用奇异值分解来帮助求解最小二乘问题，特别是在方程系数矩阵不满秩的情况下。-SGELSD computes the minimum-norm solution to a real linear least * squares problem: * minimize 2-norm(| b- A*x |) * using the singular value decomposition (SVD) of A. A is an M-by-N * matrix which
dianziping
- 电子屏字符显示器 * * "电子设计" * * 2001.10.23 * ************************* 四个显示字符数据表放在50H-6FH单元内，字符用8*8点阵，R4（30H）用于 控制显示静止字的时间，R5（31H）静止字显示跳转地址步距，B内放显示首址-E-screen character display* * " electronic design" * * 2001.10.23* ******************
adc3
- take a input vector which is no of users of b[kx1] and code vector which will be a matrix now s=[Nxk],where then give to matched filter,non-correlating detector, by randomised sequence.-take a input vector which is no of users of b[kx1] and
3
- 1、随机生成一个5*5矩阵A，元素符合均匀分布；再随机生成一个5*5矩阵B，元素符合正态分布。对A和B进行加、减、乘、除、比较等矩阵运算，查看运算结果。 2、创建5阶魔术矩阵，求A的行列式、特征值、逆、秩、迹、条件数。 3、假设矩阵 ，求A的LU分解、正交分解、特征值分解、奇异值分解。 4、创建6阶单位稀疏矩阵，并显示其全部元素。 -1, randomly generated a 5* 5 matrix A, elements in line with uniform
work
- matlab 关于association rule 的自己写的函数，有3个文件, association.m：h = association(m, i, j) i=>j, m是数据，h是support和confidence，该函数只适用于单个数据 ass_item: h=ass_itset(m, a, b) 同上，但是可用于多个数据（m为数组） assrule: h = assrule(m, threshold1, threshold2) 该函数用于c
maseidelghhhhhhh
- 用途：用Gauss-Seidel迭代法解线性方程组Ax=b 格式：x=maseidel(A,b,x0,ep,N) A为系数矩阵，b为右端向量， -Uses: The Gauss-Seidel iteration method for solving linear equations Ax = b Format: x = maseidel (A, b, x0, ep, N) A as the coefficient matrix, b for the right-hand side vec