基因算法用在图像特征提取上

This file (the project file) contains information at the project level and is used to build a single project or subproject. Other users can share the project (.dsp) file, but they should export the makefiles locally.

基于隐马尔可夫模型的学习推理的具体示例教程。(Learning reasoning based on hidden markov model concrete examples of tutorial )

MATLAB是解释型语言,运行速度比较慢.本文探讨提高MATLAB程序运行效率的若干手段.(MATLAB is an interpreted language, run more slowly. Article explores improve the operating efficiency of the program MATLAB several means.)

maple写的基本kalman算法，可以做入门参考理解(maple write basic kalman algorithm, can be understood to do entry-reference)

利用Matlab和SPSS软件实现聚类分析 运用Matlab中的一些基本矩阵计算方法，通过自己编程实现聚类算法，在此只讨论根据最短距离规则聚类的方法。(Matlab and SPSS software to achieve some basic matrix calculation method in the use of cluster analysis in Matlab, through their own programming clustering algorithm only discuss this clustering method based on the shortest distance rules.)

有关于数字通信系统中相关信号的调制解调，包括高阶PSK调制和对应解调，加噪声等(fas )

自主学习把稀疏自编码器和分类器实现结合。先通过稀疏自编码对无标签的5-9的手写体进行训练得到最优参数，然后通过前向传播，得到训练集和测试集的特征，通过0-4有标签训练集训练出softmax模型，然后输入测试集到分类模型实现分类。(Independent Learning the encoder and the sparse classifiers achieve the combination. First through sparse coding since no label was handwritten 5-9 training obtain the optimal parameters, and then through the front propagation, get the training and test sets of features, a label by 0-4 trained softmax model train set, then enter the test set to the classification model to classify.)

这段代码是关于指数信道使用QPSK。我根据不同的抽头数（即多径数）对指数信道进行编码。我还包括了使抽头的功能代码，这意味着多路径。该代码还比较了指数模型和瑞利模型。

SeisLab 3.01   This folder contains a folder, S4M, with Matlab functions for seismic and   well-log analysis, in the following collectively called SeisLab.   Folder "Other" contains support functions written by others and available from the Matlab Central Program Exchange.   A manual in PDF format explains the philosophy behind the design of the functions in SeisLab and the way they can be used. For good measure, I have   also added a few sample scripts (verba docent, exempla trahunt).   To run SeisLab functions, copy folder S4M to your computer and add   it and its subfolders to your Matlab path (please note that folders that start with "@" are class directories and need not be included   explicitly in the Matlab path; Matlab will find them anyway; the same   is true for "private" folders).     The f

The block maps each integer value (or stored integer when you use a fixed point input) to a group of M bits, using the selection for the Output bit order to determine the most significant bit. The resulting output vector length is M times the input vector length.