PIDZHENGDING

书号的识别程序，带有相应的图解。可以作为识别的参考程序。(ISBN identification procedures, with the corresponding illustrations. Identification procedure can be used as a reference.)

说明：  条件语句是编程中最常用的结构，这里作一个演示结果的文件(conditional statement is the most commonly used programming structure here for a demonstration of the results of the document)

matlab编写的用lk算法进行光流计算的程序，很好的例程(lk algorithm using matlab prepared for optical flow calculation program, a good routine)

自己编写的多项式拟合程序，阶数可选择，里面含有一个数据文件，下载即可运行，使用过程中也可以用自己的数据。(themselves prepared polynomial fitting procedure, the option order, which contains a data file can be downloaded operation, the use of the process can also use their own data.)

二阶粒子群，比pso更加精确更加灵敏地优化函数。在标准pso算法中，微粒的飞行速度仅仅是微粒当前位置的函数，而二阶粒子群算法中微粒飞行速度的变化与微粒位置的变化有关(Second swarm, more sensitive and more accurate than pso optimize function. Pso algorithm in the standard, the particle particle flight speed is only a function of the current position, the second particle swarm flying speed of particles in the particle changes related to changes in the position)

PID 的初学者模型，是关于专家型PID的，也可以用于系统PID的改进(PID beginner model is based on expert PID and PID can also be used for system improvement)

C++ code for hill climbing

信号的功率谱香农熵和功率谱指数熵 matlab 源程序(Signal power spectrum Shannon entropy and entropy matlab source power spectral index)

在高斯与一径瑞利衰落信道下对DVB-T系统的同步算法进行仿真，计算正确符号同步的概率(in Gaussian with a Rayleigh fading channel for DVB-T system synchronization algorithm simulation, correct calculation of the probability Symbol Synchronization)

function [L,C] = kmeans(X,k) KMEANS Cluster multivariate data using the k-means++ algorithm. [L,C] = kmeans(X,k) produces a 1-by-size(X,2) vector L with one class label per column in X and a size(X,1)-by-k matrix C containing the centers corresponding to each class. Version: 07/08/11 Authors: Laurent Sorber (Laurent.Sorber@cs.kuleuven.be) References: [1] J. B. MacQueen, "Some Methods for Classification and Analysis of MultiVariate Observations", in Proc. of the fifth Berkeley Symposium on Mathematical Statistics and Probability, L. M. L. Cam and J. Neyman, eds., vol. 1, UC Press, 1967, pp. 281-297. [2] D. Arthur and S. Vassilvitskii, "k-means++: The Advantages of Careful Seeding", Technical Report 2006-13, Stanford InfoLab, 2006.