wave-Cepstrum

用MATLAB进行区间估计与线性回归分析的ppt课件，希望对大家有用(Carried out using MATLAB interval estimation and linear regression analysis ppt courseware, hope for all of us)

一个编译logmmse的Mfile，可以音频净化，噪音处理以及噪音识别，比通常的wiener filter效果要好，而且实现时间短(logmmse implementation)

bhatii matmeataica 1st

本程序实现了基于相关矩阵法的LTE及4G中MIMO多径信道的仿真，最终获得了MIMO信道的传播矩阵。（第一步，选择应用场景（确定信道参数和天线配置参数）；第二步，计算相关矩阵，并做Cholesky分解；第三步,利用Jakes模型产生多普勒随机变量；第四步,形成TDL；第五步，根据前四步准备，最终得到MIMO多径信道的信道传输矩阵）(This procedure achieved based on the correlation matrix method and 4 g LTE MIMO in multipath channel simulation, finally obtained the spread of the MIMO channel matrix.)

说明：  本目录主要包括：文档和两个源代码。 其中一个源代码是识别程序的，另一个是一个矩阵类库的，书里面也使用过，所以一并附上。 因为已经保存了训练好的网络权值，所以第一次使用识别程序时，可以直接运行\\Release目录下的可执行文件，然后对图片目录中的测试图片进行读入、识别。 当然读者也可以自行用训练样本训练网络，不过要特别注意训练样本的选择，否则可能造成识别率很低。训练样本选择的原则是，尽可能的有代表性，在训练时间不至于太长的情况下，训练样本数目尽量多。 ( This directory includes: documentation and two source code. One of the source code is to identify the procedure, the other is a matrix class library, the book which is also used, so be attached. Because it has saved the trained network weights, so the first use of identification procedures, you can directly run \ \ Release directory of the executable files, and then tested on the images directory to read pictures, identification. Of course, readers are free to use the training samples to train the network, but pay special attention to training samples, otherwise it may cause very low recognition rate. Training sample selection principle is that as far as possible, representative, in the long training time Buzhi Yu s case, the number of training samples as much as possible. )

MATLAB TOOLBOX FOR PETRI NETS

说明：  利用MATLAB编写的该算法可以对小球碰撞的过程进行模拟(Prepared using the MATLAB algorithm for small ball to simulate the process of collision)

说明：  每个汉字代表1个数字（1~9）。相同的汉字代表相同的数字，不同的汉字代表不同的数字。 (Each character is representative of a number (1 to 9). The same Chinese characters represent the same number, different characters represent different numbers.)

matlab灰色模型程序，关于水压程序。(matlab gray model)

math function for learning methods