FSMCI

在本次仿真中，信道加入了高斯白噪声，收端采用了相干解调的解调方法。附详细的WORD文档说明(In this simulation, the channel joined the Gaussian white noise, closed-ended demodulation using coherent demodulation method. WORD document attached to a detailed description)

1.参数的设计 1）系统的变量比较多而且应用非常频繁，所以运用的变量要具有特征性 2）尽量使用课本上的变量，如果课本上的变量无法实现，在考虑其他自己定义变量 3）由于MATLAB使用变量可以不用先定义生命变量，所以可以直接使用变量 4）但是一些全局性的变量必须要定义( 1. Parameter design 1) system variables and the application is more frequent, so the use of the variable to have the characteristics of 2) the use of textbooks on the variables as possible, if the books can not be achieved on the variables, taking into account their other define the variable 3) use of the MATLAB variable life and variable can not be defined, so you can directly use the variable 4), but some global variables must be defined)

异步电动机反接制动，与陈伯时电机拖动课程内容相似(Asynchronous motor reverse braking Emulator)

Matlab algorithm genetic implementation

模拟从二维余弦 到量化 到分割 到Z处理的过程(simulate the process of dct quantization divide and zigzag the inverse process)

说明：  最速下降梯度法matlab程序和用拟牛顿法求极小值点(apply a gradient–descent based error minimization approach to minimize the difference between the real deformation and the simulated one given the elasticity parameters)

matlab常用函数，是一些基本的函数，说明及使用(matlab function)

说明：  相关向量机（Relevance Vector Machine，简称RVM）是Micnacl E.Tipping于2000年提出的一种与SVM（Support Vector Machine）类似的稀疏概率模型，是一种新的监督学习方法。 它的训练是在贝叶斯框架下进行的，在先验参数的结构下基于主动相关决策理论（automatic relevance determination，简称ARD）来移除不相关的点，从而获得稀疏化的模型。在样本数据的迭代学习过程中，大部分参数的后验分布趋于零，与预测值无关，那些非零参数对应的点被称作相关向量（Relevance Vectors），体现了数据中最核心的特征。同支持向量机相比，相关向量机最大的优点就是极大地减少了核函数的计算量，并且也克服了所选核函数必须满足Mercer条件的缺点。(Relevance Vector Machine (RVM) is a sparse probability model similar to SVM (Support Vector Machine) proposed by Micnacl E. Tipping in 2000. It is a new supervised learning method. Its training is carried out under the Bayesian framework. Under the structure of prior parameters, it is based on Automatic Relevance Determination (ARD) to remove the irrelevant points, so as to obtain the sparse model. In the iterative learning process of sample data, the posterior distribution of most parameters tends to zero, which is independent of the predicted value. The points corresponding to non-zero parameters are called Relevance Vectors, which represent the most core features of the data. Compared with support vector machine, the biggest advantage of correlation vector machine is that it greatly reduces the computation amount of kernel function, and also overcomes the shortcoming that the selected kernel function must meet Mercer's condition.)

简单描述了在MATLAB中的一些常用的绘图语言，规则和技巧(Briefly describes some of the commonly used in the MATLAB graphics language, rules and techniques)

穿墙传播带来的误差分析例程，对于非视距的认识可以加深(error analysis about through-wall propagation)