fenghen

MATLAB-based simulators for mobile robot Simultaneous Localization and Mapping

finite difference time domain 1d code

基于MATLAB7_0软件的实时数据交换的实现(Based on the implementation of real-time data exchange software MATLAB7_0)

基于FARIMA的网络自相似业务流模型的研究与仿真 包含三个函数：av.m、far.m、zsrs.m。具体说明见论文及模型说明(Research and simulation of self-similar traffic model based on a FARIMA the network consists of three functions: av.m far.m, zsrs.m. Specific instructions, see papers and model description)

The equation is written as a system of two first order ODEs. These are evaluated for different values of the parameter Mu. For faster integration, we choose an appropriate solver based on the value of the parameter Mu. (The equation is written as a system of two first order ODEs. These are evaluated for different values of the parameter Mu. For faster integration, we choose an appropriate solver based on the value of the parameter Mu.)

绘制甘特图的一个程序，已调试通过，接收到流水车间的指派表和时间表后绘制甘特图(接收到流水车间的指派表和时间表后绘制甘特图，绘制甘特图的一个程序，已调试通过，)

说明：  一个关于duffing方程混沌的源程序，实现绘制混沌图像以及最大lyapunov参数的计算(a program to drew the figure of duffing and give a grief description of the parameter of lyapunov.)

Matlab所有函数速查手册，方便查询，比较实用(The Matlab functions Quick Reference, easy access, more practical)

应用MATLAB实现噪声滤波器。一段有尖锐噪声的音频信号去噪。( filter)

说明：  EM 算法是Dempster，Laind，Rubin于1977年提出的求参数极大似然估计的一种方法，它可以从非完整数据集中对参数进行 MLE估计，是一种非常简单实用的学习算法。这种方法可以广泛地应用于处理缺损数据、截尾数据以及带有噪声等所谓的不完全数据，可以具体来说，我们可以利用EM算法来填充样本中的缺失数据、发现隐藏变量的值、估计HMM中的参数、估计有限混合分布中的参数以及可以进行无监督聚类等等。(EM algorithm is a method for maximum likelihood estimation of parameters proposed by Dempster, Laind and Rubin in 1977. It can estimate parameters from incomplete data sets by MLE. It is a very simple and practical learning algorithm. This method can be widely used in dealing with the so-called incomplete data such as defective data, censored data and noisy data. Specifically, we can use EM algorithm to fill missing data in samples, find hidden variable values, estimate parameters in HMM, estimate parameters in finite mixed distribution, and so on. Unsupervised clustering and so on.)