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matlab课件很有用的,对matlab学习很有帮助,希望采纳谢谢(it s very useful)

在相控阵基础上，每个阵元增加一个小的频偏形成的频率分集阵列方向图(the pattern of frequency diverse array)

MATLAB基础讲课笔记内容 解压就能阅读，(Lecture notes based on the contents of MATLAB)

遗传算法（Genetic Algorithm）是模拟达尔文生物进化论的自然选择和遗传学机理的生物进化过程的计算模型，是一种通过模拟自然进化过程搜索最优解的方法，它最初由美国Michigan大学J.Holland教授于1975年首先提出来的，并出版了颇有影响的专著《Adaptation in Natural and Artificial Systems》，GA这个名称才逐渐为人所知，J.Holland教授所提出的GA通常为简单遗传算法（SGA）。(GA (Genetic Algorithm) is a simulation of Darwinian theory of evolution by natural selection and genetic mechanism of biological evolution of the computing model is a natural evolutionary process by simulating the optimal solution search method, which originally developed by the University of Michigan J.Holland Professor in 1975 first proposed and published an influential monograph " Adaptation in Natural and Artificial Systems" , GA This name was gradually known, J.Holland raised by Professor GA is usually simple genetic algorithm (SGA ).)

Matpower is a package of Matlab M-les for solving power ow and optimal power ow problems. It is intended as a simulation tool for researchers and educators that is easy to use and modify. Matpower is designed to give the best performance possible while keeping the code simple to understand and modify.

关于混沌Kolmogorov熵的计算程序，在前人基础上自己编写的。已经用过很多混沌时间序列K熵的计算。(On chaotic Kolmogorov entropy calculation procedure, based on predecessors have written. Has been used a lot of chaotic time series K entropy calculations.)

[sigma,mu,A]=mygaussfit(x,y) [sigma,mu,A]=mygaussfit(x,y,h) this function is doing fit to the function y=A * exp( -(x-mu)^2 / (2*sigma^2) )

里面是关于matlab最基本的学习资料，有两个版本的，大家可以根据自己不同的要求，去学习它。(Matlab inside on the most basic learning materials, there are two versions, we can according to their different requirements, to study it.)

NW小世界网络的构成原则为：从一个环状的规则网络开始，网络含有N个结点，每个结点向与它最近邻的K个结点连出K条边，并满足N>>K>>In(N)>>1。随后进行随机化加边，以概率p在随机选取的一对节点之间加上一条边。其中，任意两个不同的节点之间至多只能有一条边，并且每一个节点都不能有边与自身相连。改变p值可以实现从最近邻耦合网络(p=0)向全局耦合网络(p=1)转变。在p足够小和N足够大时，NW小世界模型本质上等同于WS小世界模型。 (NW constitutive principles of small-world networks: from the rules of a ring network, network with N nodes, each node is connected to the K node with its nearest neighbor K edges, and meet N > > K > > In (N) > > 1. Followed by randomization into the plus side, with probability p with an edge between a pair of nodes in a randomly selected. Wherein between any two different nodes at most only one edge, and each node can not have a side connected with itself. P value can be changed to achieve the transition from the nearest neighbor coupling network (p = 0) coupled to the global network (p = 1). When p is small enough and large enough N, NW small-world model is essentially the same as the WS small-world model.)

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