1_speed_control_pid

人工神经网络中用Oja网络求解模式的主分量问题(Oja networks using artificial neural network model of the main components of problem solving)

这是一本有关MATLAB的参考书，适合于使用MATLAB或正在打算使用MATLAB的读者。本书另辟蹊径可以借助或不借助MATLAB随带的文档资料让读者自学MATLAB。书中口语化的风格,使读者易于阅读。如书名所示,本书提供了读者精通MATLAB所需的工具。作为编程语言和可视化工具,MATLAB具有丰富的一系列功能,可解决工程、科学计算和数学学科中许多问题。本书的基本目的是通过向读者展示如何有效地使用这些功能来帮助读者增强工作能力!(This is a reference book about MATLAB, suitable for use or are planning to use MATLAB MATLAB readers. This book can open a new path with or without the help of MATLAB documentation that comes with the reader learning MATLAB. Colloquial style of the book, the reader easy to read. Such as the title indicates, this book offers readers the tools needed to master MATLAB. As a programming language and visualization tools, MATLAB has a rich range of functions, solve engineering, scientific computing and mathematical disciplines, many of the problems. The basic purpose of this book by showing readers how to effectively use these features to help readers enhance the ability to work!)

基于matlab的三层bp神经网络源程序代码(based on the three-tier bp Matlab neural network source code)

Pusle code modulation

时频分析简单例程，WV和短时变换程序.代码简便，保证可以应用(Time frequency analysis of simple routines, WV and short-time transform program)

说明：  第13章： 随机数生成 PFQZ 用平方取中法产生随机数列 MixMOD 用混合同余法产生随机数列 MulMOD1 用乘同余法1产生随机数列 MulMOD2 用乘同余法2产生随机数列 PrimeMOD 用素数模同余法产生随机数列 PowerDist 产生指数分布的随机数列 LaplaceDist 产生拉普拉斯分布的随机数列 RelayDist 产生瑞利分布的随机数列 CauthyDist 产生柯西分布的随机数列 AELDist 产生爱尔朗分布的随机数列 GaussDist 产生正态分布的随机数列 WBDist 产生韦伯西分布的随机数列 PoisonDist 产生泊松分布的随机数列 BenuliDist 产生贝努里分布的随机数列 BGDist 产生贝努里-高斯分布的随机数列 TwoDist 产生二项式分布的随机数列 (random)

code matlab dwt watermarking 3d meshs

performes convolution without using the builtin function

用matlab软件编写感知器算法，实现对样本的分类，样本点为X1(0，0)，X2(0，-1)，X3(-1，0)，X4(-1，-1) (Perceptron Approach with matlab)

PCA and PLS aims:to get some insight into the bilinear factor models Principal Component Analysis (PCA) and Partial Least Squares (PLS) regression, focusing on the mathematics and numerical aspects rather than how s and why s of data analysis practice. For the latter part it is assumed (but not absolutely necessary) that the reader is already familiar with these methods. It also assumes you have had some preliminary experience with linear/matrix algebra.