QDPSK_2

matlab编的有关响应面的程序，运算有结果(Program of response surface compiled by MATLAB)

  最小费用最大流算法通用Matlab函数 基于Floyd最短路算法的Ford和Fulkerson迭加算法   基本思路：把各条弧上单位流量的费用看成某种长度,用Floyd求最短路的方法确定一条   自V1至Vn的最短路 再将这条最短路作为可扩充路,用求解最大流问题的方法将其上的流   量增至最大可能值 而这条最短路上的流量增加后,其上各条弧的单位流量的费用要重新   确定,如此多次迭代,最终得到最小费用最大流.( Minimum cost maximum flow algorithm for general Matlab functions based on the Floyd shortest path algorithm of Ford and Fulkerson superposition algorithm of the basic ideas: the cost of unit flow on the arc as a certain length of Floyd for the most short-circuit method to determine the one percent V1 through Vn are the most short-circuit then this short circuit as a scalable road, a method for solving the maximum flow problem on flow increased to the maximum possible value that the shortest way increased traffic, the cost of unit flow on each arc sure, so many iterations, the final minimum cost maximum flow.)

在matlab中，利用GUI中各句柄之间的关系，进行相应坐标轴中图像的清除工作，对GUI设计中的初始化和重置工作很有用(In matlab using the relationship between the handle to the GUI, corresponding axis image cleanup, useful for initialization and reset the GUI design)

[精通MATLAB.6.5版]书籍附带的源代码([Proficient MATLAB.6.5 Edition] books accompanying source code)

小区间干扰协调技术研究代码，利用matlab编程实现(Inter-cell interference coordination technology research code, the use of matlab programming)

说明：  optimal P and Q of DG(disributed generator)

说明：  演示Compressive Sensing的Matlab程序 信号压缩传感的实现(正交匹配追踪法Orthogonal Matching Pursuit) (Compressive Sensing of Matlab demonstration program to achieve signal compression sensor (orthogonal matching pursuit method Orthogonal Matching Pursuit))

Matlab-Wavelet-Denoise-Compress. The practice of small noise, a very good application

一种matlab实现的频率估计函数。matlab代码(A matlab function to achieve the frequency estimation. matlab code)

粒子群优化算法(PSO)是一种进化计算技术(evolutionary computation)，有Eberhart博士和kennedy博士发明。源于对鸟群捕食的行为研究 PSO同遗传算法类似，是一种基于叠代的优化工具。系统初始化为一组随机解，通过叠代搜寻最优值。但是并没有遗传算法用的交叉(crossover)以及变异(mutation)。而是粒子在解空间追随最优的粒子进行搜索。详细的步骤以后的章节介绍 同遗传算法比较，PSO的优势在于简单容易实现并且没有许多参数需要调整。目前已广泛应用于函数优化，神经网络训练，模糊系统控制以及其他遗传算法的应用领域(Particle Swarm Optimization (PSO) is an evolutionary computation technique (evolutionary computation), has Dr. Eberhart and Dr. kennedy invention. Deriving from the behavior of birds of prey PSO with genetic algorithm is similar to an iterative optimization-based tools. System initialization for a group of random solutions, through the iterative search for optimal values. But there is no cross-genetic algorithm used (crossover) and mutation (mutation). But the particles in the solution space of the particles to follow the optimal search. In detail the steps after the introduction sections compared with the genetic algorithm, PSO has the advantage of being simple and easy and did not realize many of the parameters need to be adjusted. Has been widely applied to function optimization, neural network training, fuzzy system control, as well as other genetic algorithm applications)