power_SOFC

AUSM求解半圆面NS方程(海平面参数)(AUSM METHOD FOR CIRCULAR BODY FLOW WITH SEA-LEVEL CONDITION)

说明：  RAKE receiver. UWB radio provides a very fine time resolution

在随机微分方程中连续布朗运动的Matlab模拟(Matlab simulation of stochastic differential equations in continuous Brownian motion of)

MATLAB simulation of 3-phase PWM rectifier. Sine PWM is used as modulation technique. Modulation index is 0.8.

利用3*3模版实现对图像的膨胀,是比较基础的图像膨胀的算法(The use of 3* 3 stencil of the image to achieve the expansion, is the basis of comparison of image expansion algorithm)

1998年, Watts和Strogatz 提出了小世界网络这一概念,并建立了WS模型。 实证结果表明,大多数的真实网络都具有小世界特性(较小的最短路径) 和聚类特性(较大的聚类系数) 。 WS小世界模型构造算法 1、从规则图开始：考虑一个含有N个点的最近邻耦合网络，它们围成一个环，其中每个节点都与它左右相邻的各K/2节点相连，K是偶数。 2、随机化重连：以概率p随机地从新连接网络中的每个边，即将边的一个端点保持不变，而另一个端点取为网络中随机选择的一个节点。其中规定，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。 (In 1998, Watts and Strogatz proposed the concept of the small world network, and the establishment of the WS model. The empirical results show that most real networks have small world (the smaller the shortest path) and clustering properties (high clustering coefficient). WS small-world model construction algorithm 1, from the rules FIG: consider a nearest neighbor coupling network with N points, they are surrounded by a ring, wherein each node and its left and right adjacent to each of K/2 connected to the node, K is an even number. The 2 randomized reconnection: randomly with probability p from the new connection on each side of the network, coming section of one of the endpoints remain unchanged, while the other endpoint is taken as the random selection of a node in the network. Which states that between any two different nodes can have at most one edge, and each node can not be connected to its own side.)

关于积分分离PID的程序，可以供大家学习参考。(About integral separation PID program, everyone can learn reference.)

实现复杂网络中社区发现的评价函数-模块度(Implement modularity in complex network with matlab.)

用MATLAB实现的BPSK调制解调源程序(In MATLAB source code for BPSK modulation and demodulation)

一些常见的数据预测模型matlab源代码(Some matlab source code of common data predictive models )