MATLABjianmingjiaochang

多传感器数据融合：matlab仿真程序实现多传感器数据融合算法(Multi-sensor data fusion: matlab simulation program to achieve multi-sensor data fusion algorithm)

通信系统防真，用matlab编写，ＭＣＱＰＳＫ(communication system simulation by matlab,ＭＣＱＰＳＫ)

基于导频的频偏估计算法，经典Classen算法，亲测课实现(Frequency Offset Estimation Algorithm Based on the pilot, classic Classen algorithm, pro-test lesson realization)

用matllab工具箱去实现绘图问题，可直接运行(To complete the huitu problem by matlab language)

super code for matlab endpoints

induction machine model in matlab coding without simulink

可以求解MATLAB VRP问题中的距离问题 省去输入的麻烦(It can solve the distance problem in MATLAB VRP problem and save the trouble of input.)

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.)

说明：  例题全部代码. 薛定宇, 陈阳泉. 高等应用数学问题的Matlab求解.(All the code examples. Xue Dingyu, Chen Yangquan. MATLAB to solve higher applied mathematical problems.)

混沌系统的love-model的MATLAB程序(Love-model chaotic system MATLAB program)