3.Root-MUSIC-MATLAB

Tool for compress and decompress for image, using differential coding prediction

基于Matlab的小波去噪程序，和大家共同讨论(based on Matlab wavelet denoising procedures, and to discuss common)

函数逼近教学例子,可以选择不同训练算法。(Function approximation teaching example, you can choose different training algorithms.)

心电、脉搏信号的提取和处理，心电脉搏特征波提取(Extraction and Processing of ECG and Pulse Signal and Extraction of ECG Pulse Characteristic Wave)

最小均方误差（MMSE）的算法，包括随机梯度算法，相对梯度算法，包括最小二乘法、SVM、神经网络、1_k近邻法,本程序的性能已经达到较高水平，外文资料里面的源代码，感应双馈发电机系统的仿真，时间序列数据分析中的梅林变换工具，实现了对10个数字音的识别程。( Minimum mean square error (MMSE) algorithm, Including stochastic gradient algorithm, the relative gradient algorithm, Including the least squares method, the SVM, neural networks, 1 _k neighbor method, The performance of the program has reached a high level, Foreign materials inside the source code, Simulation of doubly fed induction generator system, Time series data analysis Mellin transform tool, Realization of 10 digital audio recognition progra.)

利用simulink和模糊工具箱建立模糊控制器，实现模糊控制，模糊PID控制，模型参考自适应控制等(The fuzzy controller is built by Simulink and fuzzy toolbox to realize fuzzy control, fuzzy PID control, model reference adaptive control and so on.)

采用一个改进的ip-iq的谐波检测算法，无PLL，能运行。(An improved ip-iq algorithm for harmonic detection is implemented without PLL)

一个能离散FFT的MATLAB综合运用题,做的不好(A discrete FFT of the integrated use of MATLAB title, do not)

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.)

适合初级学习MatLab的用户使用,讲解的比较清晰,很适合于学习(suitable for the initial study MatLab users, on the clear, well suited to learning)