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OFDM发射机16QAM IFFT模块MATLAB程序(failed to translate)

matlab的经典案例，小程序，适用于初学matlab的同学(matlab classic cases for student who are learning matlab)

生成一个一定参数的原信号，并附加一个符合均匀分布的随机噪声，分别画出该信号经过均匀采样的频谱图以及经过非均匀采样的频谱和时域图(Generate a certain parameter of the original signal, and attach a line with uniformly distributed random noise were drawn through uniform sampling of the signal spectrum as well as through non-uniform sampling of the spectrum and time domain)

MATLAB库函数是非常有用的帮助资料，可以帮助初学者很快熟悉MATLAB的强大功能和应用范围。(MATLAB library function is very useful help information that can help beginners quickly become familiar with MATLAB' s power and range of applications.)

Extended Kalman Filter Tracking Object in 3-D(Assume that we want to track an object moving in 3-D space with constant velocity. Our instruments observe bearing, range and high(cylindrical coordinates). However, of an interest are rectangular coordinates. Since transformation is non-linear this requires use of extended Kalman filter. Because transformation is non-linear between X,Y and Range,Bearing and linear between Z and high(Z is height), this problems serves as a good comparason of how well extended Kalman filter can perform. By comparing its linear estimation error in Z to non-linear estimations in X and Y, we can judge how non-familiarities effect estimation. )

data converters by maloberti

Data files are in Matlab format. Each file contains fan and drive end vibration data as well as motor rotational speed. For all files, the following item in the variable name indicates: DE - drive end accelerometer data FE - fan end accelerometer data BA - base accelerometer data time - time series data RPM- rpm during testing

基于遗传算法，进而优化惩罚因子，核函数参数的的支持向量机进行分类(GA SVM classification)

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.)

一种基于神经网络模型的matlab实现程序(Based on neural network model matlab implementation procedures)