1.Bayesian-Reasoning-and-Machine-Learning

说明：  一篇全面介绍Dantzig selector的英文文章，37页，用于压缩感知的重构(The Dantzig selector: statistical estimation when p is much larger than n)

some important ip code

matlab教案：第1章 MATLAB操作基础第2章 MATLAB矩阵及其运算第3章 MATLAB程序设计第4章 MATLAB文件操作第5章 MATLAB绘图第6章 MATLAB数据分析与多项式计算(matlab lesson plan: Chapter 1 Chapter 2 MATLAB operating foundation MATLAB matrix arithmetic Chapter 3 MATLAB program design Chapter 4 MATLAB file operation Chapter 5 Chapter 6 of the MATLAB graphics MATLAB data analysis and polynomial)

levy飞行改进pso,更好的pso改进措施，大家可以看看(levy_Flight to improve pso)

该文件是关于采样和量化的一些资料，对于加深采样量化概念的理解具有一定的参考价值。(The document is on the sampling and quantification of some of the information, for better understanding of the concept of sampling to quantify a certain reference value.)

递归式最小均方（RLS）算法的基本思想是力图使在每个时刻对所有已输入信号而言重估的平方误差的加权和最小，这使得RLS算法对非平稳信号的适应性要好。与LMS算法相比，RLS算法采用时间平均，因此，所得出的最优滤波器依赖于用于计算平均值的样本数，而LMS（NLMS）算法是基于集平均而设计的，因此稳定环境下LMS（NLMS）算法在不同计算条件下的结果是一致的(Recursive least-mean-square (RLS) algorithm for the basic idea is to try to make in every moment of all the input signal in terms of re-evaluation of the weighted squared error and the smallest, which allows non-stationary RLS algorithm for adaptive signal better. Compared with the LMS algorithm, RLS algorithm uses the average time, therefore, the resulting optimal filter depends on the used to calculate the average number of samples, and the LMS (NLMS) algorithm is designed based on set average, and therefore a stable environment LMS (NLMS) algorithm in different conditions, the results of the calculation is consistent)

用MATLAB实现遗传算法的程序，可以无限制的套用，仅供参考！(it is good!)

基于sch干扰消除的机理，提出一种改进的sch干扰消除方案，以降低sch干扰消除的计算量和实现复杂度。(Based on sch interference cancellation mechanism, an improved interference sch eradication programs to reduce the sch interference to eliminate the computation and implementation complexity. )

说明：  generation of AM signal,,simulink

卡尔曼的视频序列帧图像的跟踪程序，基于matlab语言的，大家可以看看！(Tracking procedure of the Kalman frame of video sequences)