PV_MPPT

this code reduce the dimentional of feature space using combine sparse matrix+PCA for classification eeg signal. more detail exixst inside the code. this code tested and work properly

动力学分析的五种算法，分别是模态叠加法、Newmark积分算法、中心差分算法等m文件代码(m-codes about dynamic analysis )

《matlab从入门到精通》全书示例的源代码，帮助大家更好的学习matlab("Matlab from entry to the proficient," the book s source code examples to help you better learn matlab)

数值计算方法实例 图像处理 文件包 数值计算方法实例 图像处理 文件包(MAtlab image opertion)

LTE 64QaM 实现，matlab算法(development of LTE 64Qam)

基于vs+MATLAB的icp基本演算法(Based on the icp vs+ MATLAB basic algorithm)

希尔伯特包络谱，程序简单易懂，适合初学的人学习(Hilbert envelope spectrum, straightforward procedures, suitable for beginners to learn)

说明：  pq法的谐波检测，谐波源是三相全桥整流电路(Pq method of harmonic detection, harmonic source is a three-phase full-bridge rectifier circuit)

matlab自动代码生成的源程序，和飞思卡尔mc9s12dg128集成(matlab code)

The main features of the considered identification problem are that there is no an a priori separation of the variables into inputs and outputs and the approximation criterion, called misfit, does not depend on the model representation. The misfit is defined as the minimum of the l2-norm between the given time series and a time series that is consistent with the approximate model. The misfit is equal to zero if and only if the model is exact and the smaller the misfit is (by definition) the more accurate the model is. The considered model class consists of all linear time-invariant systems of bounded complexity and the complexity is specified by the number of inputs and the smallest number of lags in a difference equation representation. We present a Matlab function for approximate identification based on misfit minimization. Although the problem formulation is representation independent, we use input/state/output representations of the system in order (The main features of the considered identification problem are that there is no an a priori separation of the variables into inputs and outputs and the approximation criterion, called misfit, does not depend on the model representation. The misfit is defined as the minimum of the l2-norm between the given time series and a time series that is consistent with the approximate model. The misfit is equal to zero if and only if the model is exact and the smaller the misfit is (by definition) the more accurate the model is. The considered model class consists of all linear time-invariant systems of bounded complexity and the complexity is specified by the number of inputs and the smallest number of lags in a difference equation representation. We present a Matlab function for approximate identification based on misfit minimization. Although the problem formulation is representation independent, we use input/state/output representations of the system in order)