Convolutional_and_puncturing

第四版prokias数字通信之配套代码第九章(source code(matlab )for prokias Digital Communication (4th edition))

说明：  离散频谱的全相位校正法 离散频谱的全相位校正法.rar(Discrete spectrum of the full phase correction method. Rar)

实现永磁同步电机的控制，采用矢量控制的方法，效果显著(Realize the control of permanent magnet synchronous motor )

GSA,source code for Gravitational Search algorithm

非常有用的matlab cheng xu (very useful Matlab WM xu)

color space converter

谱聚类，效率不错的聚类算法.spectral clustering(spectral clustering)

利用matlab GUI实现的串口编程例子，对于初学者具有参考意义，小波包分析提取振动信号中的特征频率。( Use serial programming examples matlab GUI implementation, For beginners with a reference value, Wavelet packet analysis to extract vibration signal characteristic frequency.)

PID控制算法的MATLAB仿真其中参数,Kp,Ki,Kd分别为PID控制器的比例、微分和积分参数。(PID control algorithm of the MATLAB simulation in which the parameters, Kp, Ki, Kd are the proportion of PID controller, differential and integral parameters.)

蕞优分组周游路线问题是一类物流智能调度问题，是众多的计算难度极大的组合爆炸问题之一。自从运输问题最早是由Hitchcock在1941年提出的[2][4]后，人们对这一类问题给予了极大的关注并进行了大量的研究，提出了有效解决问题的优化算法——单纯形法的变形[2]。但由于问题规模的扩大，纯数学方法在允许的时间和空间上都很难找到问题的最优解，于是人们开始转向寻找问题的近似最优解，遗传算法以其在求解最优化问题中的独特的自组织性、自适应性很快进入人们的视野。 经典的遗传算法对于求解多目标的NP完全性问题非常有效，但对于有多个限制条件的多目标最优化问题缺显得有点力不从心，很难得到稳定度较高、收敛较快的解。本文提出的双层遗传算法模型给这一类问题提供了一个很好的解决方法，虽然建模条件限制了描述的问题的通用性，有待推广，但它能有效利用问题的领域知识，提高了求解效率，保证了解的可行性和最优性，算法思想极具推广潜力，可在其它物流智能调度问题求解算法设计中借鉴。(The most optimal packet travel route problem is a kind of intelligent logistic scheduling problem is one of combinatorial calculation difficult problem many. Since the transportation problem was first proposed by Hitchcock in 1941 after the [2][4], the people of this class of problems given great attention and made a lot of research, the deformation of [2] optimization, simplex method algorithm is effective to solve the problem of. But due to the expansion of the scale of the problem, the pure mathematical methods in the allowed time and space are very difficult to find the optimal solution of the problem, so people begin to turn to the approximate optimal solution for the problem, genetic algorithm in solving optimization problems with its unique in the self-organizing, adaptive soon entered people s field of vision. Very effective classical genetic algorithm for solving multi-objective NP complete problems, but for the multi-objective optimization problem with multiple constraints. I)