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matlab数理统计工具箱应用简介 统计，神经网络(matlab toolbox application of mathematical statistics Introduction to statistics, neural networks)

主要是感知器程序集，用于模式分类，欢迎您的访问，谢谢您的支持和下载 (Sensor assembly is mainly used for pattern classification, welcome your visit, thank you for your support and download)

在保密数据传递等应用中，有时采用将一句话或一段文字或一段音乐隐藏在另一段音乐中，然后利用专门的算法来提取所传的信息(Data transfer in applications such as security, sometimes using the word or a text or a piece of music hidden in another section of music, and then use a special algorithm to extract the information transmission)

基于MATLAB的遗传算法 本文是以Word形式给出的 里面都是计算GA的原代码(MATLAB-based genetic algorithm given in this article is based on Word form which is basically the original code )

CO-KRIGING WITH RANDOM METHOD. 随机结合克里金方法。在地学中有很大的应用潜力，比如建模及矿物分布估计。(a variant of cokriging, in which the mean values of the vector components are related by linear combinations with known coefficients. Equations for the cokriging predictor and for the variance–covariance matrix of prediction errors are presented. A set of computer programs is provided and illustrated with applications to mineral resources evaluation, in which the proposed cokriging variant compares favorably with traditional approaches.)

matlab中，时频峰值滤波处理地震记录时的窗长讨论代码(matlab window length, the time-frequency peak filtering processing of seismic records of discussions code)

说明：  雅克比迭代法求解方程组;雅克比迭代法就是众多迭代法中比较早且较简单的一种，其命名也是为纪念普鲁士著名数学家雅可比。雅克比迭代法的计算公式简单，每迭代一次只需计算一次矩阵和向量的乘法，且计算过程中原始矩阵A始终不变，比较容易并行计算。(Jacobian iterative method for solving equations Jacobi iteration is one of the earlier and simpler iteration methods, and its name is also in memory of Jacobi, a famous mathematician in Prussia. Jacobian iterative method has a simple calculation formula, each iteration only needs to calculate matrix and vector multiplication once, and the original matrix A is always the same in the calculation process, so it is easy to parallel calculation.)

Geodetic Transformations Toolbox

处理lasso regression。 使用stochastic Markov property。(Algorithm for solving the Lasso problem: 0.5* (y- X*beta) *(y- X*beta)+ lambda* ||beta||_1 where ||beta||_1 is the L_1 norm i.e., ||beta||_1 = sum(abs( beta )) We use the method proposed by Fu et. al based on single co-ordinate descent. For more details see GP s notes or the following paper: Penalized Regressions: The Bridge Versus the Lasso Wenjiang J. FU, Journal of Computational and Graphical Statistics, Volume 7, Number 3, Pages 397?416, 1998 )

this is a gud graph....method