sepsources

说明：  matlab的遗传算法实现，学习的好帮手(matlab genetic algorithm, learning a good helper)

This is the m file to read or uff file using matlab, uff file to generate. It is sometimes convenient to issue read, create, so that if someone put a data value.

Simulation phase induction machine

本压缩包里的文件包括MATLAB编写的数字识别程序以及所用的数字样本，供大家参考。(Number Recognition)

初级matlab中的simulink学习用书，很好用(Simulink in matlab primary learning books, good with)

光纤通信中用来计算相对误差的matlab源程序，对于刚入门的人有很大的帮助。(Optical Fiber Communication is used to calculate the relative error of the matlab source code, for people who have just started a great help.)

第一步：首先我们需要强大的SNMP信息收集和整理工具，网络中这类工具比较多，如果我们只是监控的话建议使用小巧的monomon即可，当然如果企业内部要自定义建立流量监控站点或服务中心的话可以使用其他支持SNMP的MRTG，PRTG等工具。我们可以到http://monomom.matf.de/download.php地址下载该程序，整个程序只有一个文件(Step one: First of all, we need strong SNMP information to collect and organize tools, network tools such as more, if we only recommend the use of surveillance, then compact monomon can, of course, if the custom-house to set up traffic monitoring site or service center If you can use other support SNMP-MRTG, PRTG, such as instrument. We can http://monomom.matf.de/download.php address to download the program, the whole process only one document)

此算法对阵元信号进行估计，仿真结果显示其有效性(dmusic estimation)

matlab编程基础，介绍matlab编程语言的ppt，方便matlab学习(matlab Programming on the matlab programming language ppt, easy to learn matlab)

共轭梯度法（Conjugate Gradient）是介于最速下降法与牛顿法之间的一个方法，它仅需利用一阶导数信息，但克服了最速下降法收敛慢的缺点，又避免了牛顿法需要存储和计算Hesse矩阵并求逆的缺点，共轭梯度法不仅是解决大型线性方程组最有用的方法之一，也是解大型非线性最优化最有效的算法之一。 在各种优化算法中，共轭梯度法是非常重要的一种。其优点是所需存储量小，具有步收敛性，稳定性高，而且不需要任何外来参数(Conjugate Gradient method (Conjugate Gradient) is between the steepest descent method between Newton method and a method, it only USES a derivative information, but overcome the steepest descent method slow convergence of weakness, but also avoid the Newton law needs to storage and computing Hesse inverse matrix and shortcomings, Conjugate Gradient method is not only solve linear equations with most of the large method, and also one of the most effective solution large nonlinear optimization of one of the algorithm. In all kinds of optimization algorithm, the conjugate gradient method is very important. Its advantage is the storage capacity needed, it has small step convergence, high stability, and doesn t require any exotic parameters numerical experiment, this is the modern scientific computing of the answer above problem sets)