matlab_tulun

高斯hermit粒子滤波器，及其测试程序。可以用于目标跟踪。(Gaussian hermit particle filter, and testing procedures. Can be used for target tracking.)

FIR and IIR RLS algorithms are implemeted by using reference information from Simon Haykin s Adaptive Filter Theory, where a transversal filter is used for modeling the adaptive filter

GMRES算法，求解大型稀疏线性方程的快速数值解法，方便实时求解(GMRES algorithm is prosed to solve Large- scale sparse linear equations)

说明：  采用改进的蚁群智能算法，得到车辆最短路径问题快速有效的局部最优解。(The improved ACO to obtain the shortest path problem vehicle fast and effective local optima.)

数学建模与数学实验（第3版），各种常用的算法，ppt，M文件，各算法后附有matlab程序，可以方便的运行。(Mathematical modeling and mathematical experiment (3), a variety of commonly used algorithms, ppt, M file attached to each algorithm matlab program, easy to run.)

matlab 课件 很全面的 适合初学者使用 适合数模(matlab very comprehensive courseware is suitable for beginners to use for digital to analog)

车牌定位程序，自己写的车牌定位的Matlab算法，能把车牌切分提取出来。(Locate the license plate program, write your own license plate location of the Matlab algorithm, can license plate segmentation out.)

一个二维静电场，电位函数为φ（x, y），将正方形分成25个正方形网格。有16个内部网格点。假定16个网格点的初值都定为零，试用超松弛法确定16个内部网格点的电位值。(A two dimensional electrostatic field, the potential function for phi (x, y), the square into 25 square grid. There are 16 internal grid point. Assume that 16 mesh point of initial value at zero, trial overrelaxation method to determine the potential value of 16 internal grid point.)

一个实例搞定MATLAB界面编程，作者彭军(The author is pengjun,One instance to solve the MATLAB interface programming)

In this paper, we introduce a new machine-learning-based data classification algorithm that is applied to network intrusion detection. The basic task is to classify network activities (in the network log as connection records) as normal or abnormal while minimizing misclassification. Although different classification models have been developed for network intrusion detection, each of them has its strengths and weaknesses, including the most commonly applied Support Vector Machine (SVM) method and the Clustering based on Self-Organized Ant Colony Network (CSOACN). Our new approach combines the SVM method with CSOACNs to take the advantages of both while avoiding their weaknesses. Our algorithm is implemented and evaluated using a standard benchmark KDD99 data set. Experiments show that CSVAC (Combining Support Vectors with Ant Colony) outperforms SVM alone or CSOACN alone in terms of both classification rate and run-time efficiency.