382399785texture_code_and_thesis

说明：  关于控制系统分析及应用在matlab软件中编程，此书有很大的参考价值(Application on the control system analysis and programming in matlab software, book has great reference value)

说明：  几种类型的滤波小程序，可以去看看，全局去噪，阈值去噪(Several types of filtering applets, you can see, global denoising, threshold denoising)

根据像素点进行圆匹配。根据像素点进行圆匹配。(According to the pixel round match. According to the pixel round match.)

弹奏青花瓷的matlab代码 (Blue and white porcelain matlab program play)

非线性振动分析计算程序，以轴承部分参数为变量的分叉图(the calculation program of the analysis of nonlinear vibration)

说明：  matlab的bp神经网络算法以及准确率的计算，训练函数采用lm算法(Matlab bp neural network algorithm and accuracy calculation, training function using LM algorithm)

ACF中包含两个m文件，一个是自协方差函数（可单独使用）。另一个是自相关函数计算，其调用autocov函数。(ACF contains two m files, one is self-covariance function (which can be used alone). The other is calculated from the correlation function, which calls autocov function.)

关于小波边缘的matlab程序，希望对人有用(Matlab program on wavelet edge, in the hope useful to people)

说明：  EM 算法是Dempster，Laind，Rubin于1977年提出的求参数极大似然估计的一种方法，它可以从非完整数据集中对参数进行 MLE估计，是一种非常简单实用的学习算法。这种方法可以广泛地应用于处理缺损数据、截尾数据以及带有噪声等所谓的不完全数据，可以具体来说，我们可以利用EM算法来填充样本中的缺失数据、发现隐藏变量的值、估计HMM中的参数、估计有限混合分布中的参数以及可以进行无监督聚类等等。(EM algorithm is a method for maximum likelihood estimation of parameters proposed by Dempster, Laind and Rubin in 1977. It can estimate parameters from incomplete data sets by MLE. It is a very simple and practical learning algorithm. This method can be widely used in dealing with the so-called incomplete data such as defective data, censored data and noisy data. Specifically, we can use EM algorithm to fill missing data in samples, find hidden variable values, estimate parameters in HMM, estimate parameters in finite mixed distribution, and so on. Unsupervised clustering and so on.)

RPCA (Robust Principal Component Analysis)是目前用于矩阵填充、图像去噪的最有效的优化方法。该代码是求解RPCA的一种数值算法——Exact ALM（Exact Augmented Lagrange Multiplier）(The most basic form of the exact ALM function is [A, E] = exact_alm_rpca(D, λ), and that of the inexact ALM function is [A, E] = inexact_alm_rpca(D, λ), where D is a real matrix and λ is a positive real number. We solve the RPCA problem using the method of augmented Lagrange multipliers. The method converges Q-linearly to the optimal solution. The exact ALM algorithm is simple to implement, each iteration involves computing a partial SVD of a matrix the size of D, and converges to the true solution in a small number of iterations. The algorithm can be further speeded up by using a fast continuation technique, thereby yielding the inexact ALM algorithm.)