Matlab数学建模经典案例实战源程序

这个程序是用ｍａｔｌａｂ程序编写的应用数值方法计算二维平板传热的一个应用程序，含有源代码和公式，可以根据具体参加和情况进行二次开发，简单使用，非常方便。(use the Matlab to build a code,use numirical method to solve 2-D heat transfer problem, cold in the pack,can be modified by your need)

Files in matlab source code for watermarking techniqe

Microsoft Windows 2008 - The Complete Reference. Reference manual from the basics.

本例用五点差分法求解一个二阶偏微分方程，其间用Jacobi迭代求解(This example uses a five-point difference method for solving second order partial differential equations, during which the Jacobi iterative)

matlab图像分割算法源码.图像反转.灰度线性变换.非线性变换.直方图均衡化 (matlab image segmentation algorithm source code)

用matlab写的最近邻和K近邻法分类器，简单易懂，适合初学者(Written with matlab and K-NN nearest neighbor classifier, easy to understand for beginners)

这个是对16QAM各种界的一个整理，给出了仿真程序，对于初学者来说具有重要的意义。(This is a finishing 16QAM various circles, the simulation program, for beginners is of great significance.)

现代雷达系统日益变得复杂，难以用简单直观 的分析方法进行处理，往往需要借助计算机来完成 对系统的各项功能和性能的仿真。利用计算机来进 行雷达系统的仿真具有方便、灵活以及经济的特点 而 Matlab 提供了强大的仿真平台，可以为大多数雷 达系统的仿真提供方便快捷的运算。以典型的雷达 为例，它主要由天线、发射机、接收机、信号处理 机、伺服系统和终端设备等部分构成。本文主要讨 论的是雷达信号处理部分，并结合某脉冲压缩雷达 信号处理系统的实例来说明Matlab在雷达信号处理 系统仿真中的应用。 ()

Algorithme Pivo and PivoGauss in resolution of polynomial equation

小波神经网络的源程序: 1.构造的非线性函数: 位于nninit_test.m 2.直接用WNN逼近非线性:Wnn_test.m, (内部调用小波函数) 3.遗传算法优化后逼近 :GA_Wnn_test.m (内部调用遗传算法的,初始化,适应度,解码函数)-genetic algorithm optimization WNN source : 1. Construction of the nonlinear function : nninit_test.m at 2. WNN directly with nonlinear approximation : Wnn_test.m. (internal called wavelet function) 3. Genetic Algorithm optimization approach : GA_Wnn_test.m (internal called genetic algorithms, initialize, fitness and decoding functions) (Wavelet neural network source code: 1. Construction of the nonlinear function: at nninit_test.m 2. Wnn the direct use of nonlinear approximation: Wnn_test.m, (internal call wavelet function) 3. Genetic algorithm optimized approximation: GA_Wnn_test. m (internal call genetic algorithms, initialization, fitness, decoding function)-genetic algorithm optimization WNN source: 1. Construction of the nonlinear function: nninit_test.m at 2. WNN directly with nonlinear approximation: Wnn_test.m. ( internal called wavelet function) 3. Genetic Algorithm optimization approach: GA_Wnn_test.m (internal called genetic algorithms, initialize, fitness and decoding functions))