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[Other] INTRODUCTION TO MATLAB FOR

Description: 1 Tutorial lessons 1 1 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Basic features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 A minimum MATLAB session . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3.1 Starting MATLAB . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3.2 Using MATLAB as a calculator . . . . . . . . . . . . . . . . . . . . . 4 1.3.3 Quitting MATLAB . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.4 Getting started . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.4.1 Creating MATLAB variables . . . . . . . . . . . . . . . . . . . . . . . 5 1.4.2 Overwriting variable . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.4.3 Error messages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.4.4 Making corrections . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.4.5 Controlling the hierarchy of operations or precedence . . . . . . . . . 6 1.4.6 Controlling the appearance of °oating point number . . . . . . . . . . 8 1.4.7 Managing the workspace . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.4.8 Keeping track of your work session . . . . . . . . . . . . . . . . . . . 9 1.4.9 Entering multiple statements per line . . . . . . . . . . . . . . . . . . 9 1.4.10 Miscellaneous commands . . . . . . . . . . . . . . . . . . . . . . . . . 10 1.4.11 Getting help . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 1.5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2 Tutorial lessons 2 12 2.1 Mathematical functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.1.1 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.2 Basic plotting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.2.1 overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.2.2 Creating simple plots . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.2.3 Adding titles, axis labels, and annotations . . . . . . . . . . . . . . . 15 2.2.4 Multiple data sets in one plot . . . . . . . . . . . . . . . . . . . . . . 16 2.2.5 Specifying line styles and colors . . . . . . . . . . . . . . . . . . . . . 17 2.3 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.4 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.5 Matrix generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.5.1 Entering a vector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.5.2 Entering a matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2.5.3 Matrix indexing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.5.4 Colon operator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.5.5 Linear spacing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.5.6 Colon operator in a matrix . . . . . . . . . . . . . . . . . . . . . . . . 22 2.5.7 Creating a sub-matrix . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.5.8 Deleting row or column . . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.5.9 Dimension . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.5.10 Continuation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 2.5.11 Transposing a matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 2.5.12 Concatenating matrices . . . . . . . . . . . . . . . . . . . . . . . . . . 26 2.5.13 Matrix generators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.5.14 Special matrices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 2.6 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 3 Array operations and Linear equations 30 3.1 Array operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.1.1 Matrix arithmetic operations . . . . . . . . . . . . . . . . . . . . . . . 30 3.1.2 Array arithmetic operations . . . . . . . . . . . . . . . . . . . . . . . 30 3.2 Solving linear equations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.2.1 Matrix inverse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3.2.2 Matrix functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 3.3 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 4 Introduction to programming in MATLAB 35 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 4.2 M-File Scripts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 4.2.1 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 4.2.2 Script side-eRects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 4.3 M-File functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 4.3.1 Anatomy of a M-File function . . . . . . . . . . . . . . . . . . . . . . 38 4.3.2 Input and output arguments . . . . . . . . . . . . . . . . . . . . . . . 40 4.4 Input to a script ¯le . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 4.5 Output commands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 4.6 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 5 Control °ow and operators 43 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 5.2 Control °ow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 5.2.1 The ``if...end'' structure . . . . . . . . . . . . . . . . . . . . . . . 43 5.2.2 Relational and logical operators . . . . . . . . . . . . . . . . . . . . . 45 5.2.3 The ``for...end'' loop . . . . . . . . . . . . . . . . . . . . . . . . . 45 5.2.4 The ``while...end'' loop . . . . . . . . . . . . . . . . . . . . . . . 46 5.2.5 Other °ow structures . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 5.2.6 Operator precedence . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 5.3 Saving output to a ¯le . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 5.4 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 6 Debugging M-¯les 49 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 6.2 Debugging process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 6.2.1 Preparing for debugging . . . . . . . . . . . . . . . . . . . . . . . . . 50 6.2.2 Setting breakpoints . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 6.2.3 Running with breakpoints . . . . . . . . . . . . . . . . . . . . . . . . 50 6.2.4 Examining values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 6.2.5 Correcting and ending debugging . . . . . . . . . . . . . . . . . . . . 51 6.2.6 Ending debugging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 6.2.7 Correcting an M-¯le . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
Platform: | Size: 258288 | Author: taffy320 | Hits:

[Other] BAM_NN

Description: 用外积和法设计的权矩阵，不能保证p对模式全部正确的联想。若对记忆模式对加以限制（即要求p个记忆模式Xk是两两正交的），则用外积和法设计的BAM网具有较好的联想能力。 在难以保证要识别的样本（或记忆模式）是正交的情况下，如何求权矩阵，并保证具有较好的联想能力？这个问题在用BAM网络实现对字符的识别程序仿真中得到体现。我们做过尝试，用伪逆法求权矩阵，虽然能对未加干扰的字符全部进行识别，但对加有噪声的字符识别效果很差。至于采用改变结构和其他算法的方法来求权矩阵，将是下一步要做的工作。-foreign plot and the design of the power matrix, p is no guarantee that all the correct pattern association. If memory model, the limit (that is, p-memory model Xk is orthogonal to the February 2), then foreign plot and design of the BAM network has good ability to think. It is difficult to ensure the samples to identify (or memory mode) is orthogonal circumstances, the right to seek ways matrix, and to ensure that the association has good ability? The problem with the BAM network of characters identification procedures simulation can be manifested. We did try to use pseudo- inverse matrix for the right, although they would not increase the interference of the characters in the identification of all, However, a pair of noise increases the effects of poor character recognition. As for the
Platform: | Size: 231424 | Author: 东方云 | Hits:

[Algorithm] lanczos_mine

Description: 对称矩阵的lanczos算法matlab程序-symmetric matrix algorithm Matlab procedures lanczos
Platform: | Size: 1024 | Author: 韩国锋 | Hits:

[Special Effects] inverseofmatrix

Description: 采用matlab求解矩阵的逆.本程序采用完全的c语言格式在matlab平台下编制-Using matlab to solve the inverse matrix. This procedure completely the c language format in the preparation of matlab platform
Platform: | Size: 1024 | Author: luozhenbao | Hits:

[Mathimatics-Numerical algorithms] power_method

Description: 幂法与反幂法求解矩阵特征值的C语言算法实现:本代码以Hilbert矩阵为计算对象，单纯运用C语言进行矩阵的操作以实现幂法与反幂法求解矩阵特征值的算法，应用Matlab软件对计算结果进行检验，计算结果准确无误。-Power law and inverse power method to solve matrix eigenvalue of the C language algorithm: Hilbert matrix of the code for the purpose of calculating the target, simple to use C language to realize the operation of matrix power method and inverse power method to solve matrix eigenvalue algorithm, application Matlab software for calculating the results of testing the accuracy of calculation results.
Platform: | Size: 10240 | Author: Michael_M | Hits:

[Algorithm] NumAna_GaussElim

Description: 这四个程序分别为高斯消去法、列主元消去法、全主元消去法解线性方程组和Gauss-Jordan消元法求矩阵的逆。 　　程序采用MATLAB语言开发，并在MATLAB6.5下测试通过。-The four procedures were Gaussian elimination, elimination method out PCA, PCA-wide elimination method solution of linear equations and Gauss-Jordan elimination method of inverse matrix. Procedures for the use of MATLAB language development, and under MATLAB6.5 test.
Platform: | Size: 5120 | Author: 高天 | Hits:

[Algorithm] inverse

Description: 用matlab写的五种矩阵求逆程序，里面含有说明文件-Using matlab to write five matrix inversion procedure, which contains documentation
Platform: | Size: 59392 | Author: wangweiming | Hits:

[Algorithm] minvbyGRE

Description: 用greville方法求逆矩阵，稍加改动可求广义逆矩阵-Greville method using inverse matrix, rectifiable modified generalized inverse matrix
Platform: | Size: 1024 | Author: yaoxie | Hits:

[matlab] inv

Description: 该程序能够对于矩阵就行求逆运算，是一个质量比较高的程序。-The program can be on the line for the matrix inverse operation is a relatively high quality of procedures.
Platform: | Size: 2048 | Author: 张晨 | Hits:

[matlab] fdct_usfft_cpp

Description: This directory includes matlab interface of the curvelet transform using usfft. Basic functions fdct_usfft.m -- forward curvelet transform afdct_usfft.m -- adjoint curvelet transform ifdct_usfft.m -- inverse curvelet transform fdct_usfft_param.m -- returns the location of each curvelet in phase-space Useful tools fdct_usfft_dispcoef.m -- returns a matrix contains all curvelet coefficients fdct_usfft_pos2idx.m -- for fixed scale and fixed direction, returns the curvelet which is closest to a certain point on the image Demos fdct_usfft_demo_basic.m -- display the shape of a curvelet fdct_usfft_demo_recon.m -- partial reconstruction using curvelet fdct_usfft_demo_disp.m -- display all the curvelet coefficients of an image fdct_usfft_demo_denoise.m -- image denoising using curvelet-This directory includes matlab interface of the curvelet transform using usfft. Basic functions fdct_usfft.m-- forward curvelet transform afdct_usfft.m-- adjoint curvelet transform ifdct_usfft.m-- inverse curvelet transform fdct_usfft_param.m-- returns the location of each curvelet in phase-space Useful tools fdct_usfft_dispcoef.m-- returns a matrix contains all curvelet coefficients fdct_usfft_pos2idx.m-- for fixed scale and fixed direction, returns the curvelet which is closest to a certain point on the image Demos fdct_usfft_demo_basic.m-- display the shape of a curvelet fdct_usfft_demo_recon.m-- partial reconstruction using curvelet fdct_usfft_demo_disp.m-- display all the curvelet coefficients of an image fdct_usfft_demo_denoise.m-- image denoising using curvelet
Platform: | Size: 160768 | Author: daisy | Hits:

[Other] QR_LU_Eigenvalue

Description: 包括使用修正Gram-Schmit算法实现QR分解，自编LU分解、利用幂法和反幂法计算矩阵最大和最小特征值的程序。例外附有使用这些算法的例子供参考。 QR decomposition algorithm based on modified Gram-Schmit LU decomposition algorithm algorithm used to find maximum and minimum eigenvalue based on power and inverse power method and some examples are also included.-Including the use of Gram-Schmit amended QR decomposition algorithm, self-LU decomposition, the use of power law and inverse power method to calculate maximum and minimum matrix eigenvalue procedures. With the exception of the examples of the use of these algorithms for reference. QR decomposition algorithm based on modified Gram-Schmit LU decomposition algorithm algorithm used to find maximum and minimum eigenvalue based on power and inverse power method and some examples are also included.
Platform: | Size: 4096 | Author: gogomx | Hits:

[matlab] tsvd

Description: 矩阵逆运算，tsvd的算法，matlab函数程序-Matrix inverse operation, tsvd algorithm, matlab function procedures
Platform: | Size: 1024 | Author: 马晓蕾 | Hits:

[Special Effects] MATLAB

Description: 自己现在在做的论文用到灰度共生矩阵，找了好久都没找到很好用的，所以自己就编了个，不很完美，但绝对可用，已经实现过。包括灰度共生矩阵的生成以及一些特征参数。f1，二阶距。f2，对比度。f3，相关。f5，逆差距。f6，和平均。f7，和方差。f9，差平均。f10，差方差。请不吝赐教。-Now do their own papers in the gray co-occurrence matrix used to find for a long time to find no good use, so it编了个own, not very ideal, but it is available, have been implemented. Including the generation of gray-level co-occurrence matrix as well as some characteristic parameters. f1, the second order from. f2, contrast. f3, related. f5, inverse gap. f6, and the average. f7, and the variance. f9, the average difference. f10, difference variance. Please wing.
Platform: | Size: 2048 | Author: 周鹏 | Hits:

[Speech/Voice recognition/combine] ClusterData

Description: Performs hierarchical clustering of data using specified method and seraches for optimal cutoff empoying VIF criterion suggested in "Okada Y. et al - Detection of Cluster Boundary in Microarray Data by Reference to MIPS Functional Catalogue Database (2001)". Namely, it searches cutoff where groups are independent. The techinque uses an econometric approach of verifying that variables in multiple regression are linearly independent: if all the diagonal elements of inverse correlation matrix of data are less than VIF-Performs hierarchical clustering of data using specified method and seraches for optimal cutoff empoying VIF criterion suggested in "Okada Y. et al- Detection of Cluster Boundary in Microarray Data by Reference to MIPS Functional Catalogue Database (2001)". Namely, it searches cutoff where groups are independent. The techinque uses an econometric approach of verifying that variables in multiple regression are linearly independent: if all the diagonal elements of inverse correlation matrix of data are less than VIF
Platform: | Size: 2048 | Author: tra ba huy | Hits:

[Special Effects] matrix

Description: 矩阵相乘和求逆,矩阵求逆进行LV分解，测试结果与matlab一样-Matrix multiplication and inverse, matrix inverse to LV decomposition, the same test results with matlab
Platform: | Size: 3072 | Author: 胡易 | Hits:

[matlab] inverse

Description: Matrix inverse by Gauss-Jordan elimination
Platform: | Size: 1024 | Author: katore vishal | Hits:

[3G develop] Inverse

Description: 使用C编写的复数矩阵求逆，使用高斯消去法，已经和matlab结果做过对比，无误-Written in C and the complex matrix inverse, using the Gaussian elimination method, has been done and matlab results contrast, correct
Platform: | Size: 1241088 | Author: 潘潘 | Hits:

[VHDL-FPGA-Verilog] systolic--matrix-inversion

Description: DSP算法架构及设计，内容为基于systolic的上三角矩阵求逆电路的实现，里面有详尽的MATLAB/SIMULINK 仿真模型，及HDL代码和在modelsim中的仿真程序，非常不错的。-Architecture and design of DSP algorithms, based on systolic upper triangular matrix inverse circuit to achieve detailed MATLAB/SIMULINK model and the HDL code in modelsim simulation program, which is very good.
Platform: | Size: 1387520 | Author: | Hits:

[matlab] matrix-inverse-matlab

Description: 高斯消元法矩阵求逆，程序详细，仅供学习使用-Gaussian elimination of matrix inversion and detailed procedures, only to learn to use
Platform: | Size: 1024 | Author: 周崇彬 | Hits:

[Communication-Mobile] matlab代码

Description: LM 算法最小二乘法的概念，最小二乘法要关心的是对应的cost function是线性还是非线性函数，不同的方法计算效率如何，要不要求逆，矩阵的维数。(The concept of the least square method of the lm algorithm, the least square method should be concerned whether the corresponding cost function is a linear or nonlinear function, the different methods calculate the efficiency, or the inverse, the dimension of the matrix.)
Platform: | Size: 49152 | Author: 华南虎2 | Hits:
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