A_Tutorial_on_UWB

小波包在信号时频分析中的应用，计算得而他信号小波变换的时频面(Wavelet packet time-frequency signal analysis, he calculated wavelet transform time-frequency plane)

虫口模型：某模型的分岔图，详细的计算过程，里面还附有结果图( Insect population model: a model bifurcation diagram insect population model: a model of the bifurcation diagram)

基于MRAS无速度传感器的永磁同步电机的仿真模型，可以实现较好的仿真结果。(MRAS based speed sensorless permanent magnet synchronous motor simulation model, can achieve a better simulation results.)

GVF snakes implementation

EMD和VMD的MATLAB程序，此程序已经添加了注释和说明，方便您的使用。(EMD u548CVMD u7684MATD u7A0B u5E8F uFF0C u7A0B u5E8F u5DF2 u7ECF u6DFB u52A0 u4E86 u6CE8 u91CA u548C u8BF4 u660E uFF0C u65B9 u4FBF u60A8 u7684 u4F7F U7528)

Lyapunov指数随参数变化的程序，可以自行修改计算其他类型。(Lyapunov index with parameters change procedures, can be modified to calculate other types.)

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.)

Ergodic Capacity of MIMO Triply Selective Rayleigh Fading Channels

GVF SNKE 模型matlab源代码(gvf snake)

说明：  matlab SIMULINK Arabic tutorial part 5