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《visual c++matlab图像处理与识别实用案例精选》源码(" Visual c++ matlab image processing and recognition of practical cases selected" source)

说明：  一阶线性自抗扰控制ESO建模与仿真，模型搭建正确，供大家参考(First order linear ADRC ESO modeling and simulation, the model is built correctly, for your reference)

TURBO译码性能AWGN信道测试（有菜单）(TURBO AWGN channel decoding performance testing (with menu))

《面向MATLAB工具箱的神经网络理论与应用》一书中实例的所有源代码，及详细说明(ANN，matlab,hop,bp,ART)

基于核的学习方法SVM(Support Vector Machine)有很好的分类识别性能，这是在SVM的基础上改进的一个更好的SKM(Support Kernel Machine)Package.(kernel-based learning methods SVM (Support Vector Machine) are good the classification performance, This is the SVM improved on the basis of a better SKM (Support Kernel Machi ne) Package.)

Frequent Itemset Search in Matlab

final long m Time Stamp = System.current Time Millis.

Program to simulate Rayleigh fading using a p-th order autoregressive model AR(p) according to ％ Baddour s work: "Autoregressive modeling for fading channel simulation"(Program to simulate using Rayleigh fading a p-th order autoregressive model AR (p) accord % ing to Baddour's work : "autoregressive modeling for fading channel's imulation ")

diklat gabungan matlab

在一定的约束条件下，其输出与一给定函数（通常称为期望输出）的差的平方达到最小，通过数学运算最终可变为一个托布利兹方程的求解问题。维纳滤波器又被称为最小二乘滤波器或最小平方滤波器，目前是基本的滤波方法之一。维纳滤波是利用平稳随机过程的相关特性和频谱特性对混有噪声的信号进行滤波的方法，1942年美国科学家N.维纳为解决对空射击的控制问题所建立，是40年代在线性滤波理论方面所取得的最重要的成果。(Under some constraint conditions, the output with a given function (often referred to as expected output) the square of the difference between the minimum, through mathematical operation can be turned into a final taub Leeds equation to solve the problem.Wiener filter is also known as least squares filter or least square filter, is now one of the basic filter method.Wiener filtering is to use the related characteristics of stationary random process and spectrum characteristics of mixed with noise signal filtering method, scientists in the United States in 1942 n. wiener established to solve the control problem of empty shooting, is 40 s in the theory of linear filtering is the most important results.)