Adaptive-control

完整的makay的LDPC码生成和译码的程序。(Complete makay the LDPC code generation and decoding procedures.)

说明：  模拟蚂蚁和食蚁兽的生存规律。在20X20的网格世界里，每个生物每次可向四个方向移动（但不会移除网格边缘），食蚁兽会吃掉在他目标处的蚂蚁(The goal for this programming project is to create a simple 2D predator-prey simulation. In this simulation the prey are ants and the predators are doodlebugs. These critters live in a world composed of a 20x20 grid of cells. Only one critter may occupy a cell at a time. The grid is enclosed, so a critter is not allowed to move off the edges of the world. Time is simulated in time steps. Each critter performs some action every time step.)

This book is also about complexity science, which is an interdisciplinary field—at the intersection of mathematics, computer science and natural science—that focuses on discrete models of physical systems. In particular, it focuses on complex systems, which are systems with many interacting components.

我们采用交警平台！对数据进行处理！我们会努力的加油(We use traffic platform! For data processing!)

海杂波服从K杂波，K杂波在matlab中的实现(The sea clutter obey K Clutter, Clutter K implementation in matlab)

试者工作特征曲线 （receiver operating characteristic curve，简称ROC曲线），又称为感受性曲线（sensitivity curve）。得此名的原因在于曲线上各点反映着相同的感受性，它们都是对同一信号刺激的反应，只不过是在几种不同的判定标准下所得的结果而已。接受者操作特性曲线就是以虚报概率为横轴，击中概率为纵轴所组成的坐标图，和被试在特定刺激条件下由于采用不同的判断标准得出的不同结果画出的曲线。(PLOTROC Plot receiver operating characteristic. Syntax plotroc(targets,outputs) plotroc(targets1,outputs1, name1 ,targets,outputs2, name2 , ...) Description PLOTROC(TARGETS,OUTPUTS) plots the receiver operating characteristic for each output class. The more each curve hugs the left and top edges of the plot, the better the classification. PLOTROC(TARGETS1,OUTPUTS2, name1 ,...) generates multiple plots. )

分形——生成一片漂亮的树叶，通过一个矩阵迭代并不断刷新生成的图像(fractal-- generating a beautiful leaves, through an iterative matrix and constantly refresh the image)

最小二乘算法分享下，希望对大家能够有所帮助(Least-squares algorithm to share, it is hoped to be able to help everyone)

This is one part of my IPN toolbox, aiming at the reliability analysis of neuroimaging meaures

A 10th order FIR system, excited by a sum of 5 sinusoids. Assume any numeric values for system coefficients and input signal parameters. The output of the system is corrupted by Gaussian observation noise with zero mean and variance 2 σ v (consider 2 σ v = 0.1 and 1). The estimator has access to the input and noise corrupted output of the system. However, the estimator has no knowledge about the system order, its coefficient values and observation noise variance.