matlab__chinese

xpc环境下pwm波形的输入，用来搭建半实物仿真平台。(the input of PWM in xpc ,it is used in hard in loop.)

电力电子中AC-AC交流调压电路在simulink中的仿真模型(Power electronics AC-AC AC voltage regulator circuit simulation model in simulink)

morave code for matlab

非线性支持向量机,在样本基础之上，进行预测分析。可应用于底板破坏深度计算。(Nonlinear support vector machine (SVM) )

此文件为四种空间DOA估计的MATLAB程序，包括：MUSIC, ROOTMUSIC，ESPRIT和MVDR四种方法，并带有相应的仿真图，便于比较其优缺点,很有用哦(MATLAB for DOA estimate : MUSIC, ROOTMUSI,ESPRIT and MVDR,the coresponding figures are acompanied to compare the advantages and disadwantages,they are useful)

说明：  离散信源无失真信源编码（实现huffman编码） 可用于教学(Discrete source lossless source coding (to achieve huffman coding) can be used for teaching)

用MATLAB和数值逼近方法实现费米函数的简捷计算(MATLAB and numerical approximation method for the simple calculation of the Fermi function)

基于matlab 的MP3音频播放器。能够实现播放、暂停、停止等基本功能。同时可以绘制波形图。(Matlab-based MP3 audio player. Enables play, pause, stop, and other basic functions. At the same time you can draw waveforms.)

卫星地球站传输链路计算，卫星通信中，卫星损耗的计算十分重要，用于判断通信质量，以及卫星载荷的饱和程度(Satellite earth station transmission link, satellite communication, satellite loss calculation is very important for judging the quality of communication, as well as satellite payload saturation level)

说明：  高斯过程(GP)模型中推理和预测的实现。它实现了在《Rasmussen & Williams:机器学习的高斯过程》(麻省理工学院出版社，2006)和《Nickisch & Rasmussen:二进制高斯过程分类的近似》(JMLR, 2008)中讨论的算法。该函数的优点在于灵活性、简单性和可扩展性。该函数具有一定的灵活性，首先通过定义均值函数和协方差函数来确定遗传算法的性质。其次，它允许指定不同的推理过程，如精确推理和期望传播(EP)。第三，它允许指定似然函数，如高斯函数或拉普拉斯函数(用于回归)和累积逻辑函数(用于分类)。简单性是通过一个简单的函数和紧凑的代码实现的。可扩展性是通过模块化设计来保证的，允许为已经相当广泛的推理方法、均值函数、协方差函数和似然函数库轻松添加扩展。(Gaussian Processes for Machine Learning , the MIT press, 2006 and Nickisch & Rasmussen: Approximations for Binary Gaussian Process Classification , JMLR, 2008. The strength of the function lies in its flexibility, simplicity and extensibility. The function is flexible as firstly it allows specification of the properties of the GP through definition of mean function and covariance functions. Secondly, it allows specification of different inference procedures, such as e.g. exact inference and Expectation Propagation (EP). Thirdly it allows specification of likelihood functions e.g. Gaussian or Laplace (for regression) and e.g. cumulative Logistic (for classification). Simplicity is achieved through a single function and compact code.)