wuchayufanshu

近似熵的matlab实现程序Code for computing approximate entropy for a time series(Approximate entropy )

The attached file is a tutorial on matlab that will provide the necesssary guidance to those programming in matlab.

系统的结构参数和负荷情况都已给定时，调节可利用的控制变量(如发电机输出功率、可调变压器抽头等)来找到能满足所有运行约束条件的，并使系统的某一性能指标(如发电成本或网络损耗)达到最优值下的潮流分布。(Optimal power flow(OPF) is a fundamental tool in power system planning and operation.)

异步电机带转矩内环的转速磁链闭环矢量控制(With a torque loop, speed and flux loop induction motor vector control)

无线传感器网路中基于地理位置路由算法（即GPSR）在matlab上的仿真(Location-based wireless sensor network routing algorithm (ie, GPSR) simulation in matlab)

IDW距离反比加权方法，意信号卷积的运算,并且绘制图象，包含CV、CA、Single、当前、恒转弯速率、转弯模型。( IDW inverse distance weighting method, Convolution operation is intended to signal and image rendering, It contains CV, CA, Single, current, constant turn rate, turning model.)

函数逼近中两个重要的方面，勒让德逼近和列梅兹算法(two important aspects in function approximation, Legendre approximation and Remez algorithm )

加入了导频的matlab编程，有信道的衰减补偿(Joined the pilot of matlab programming, there is the channel attenuation compensation)

How to use the ordinary Voronoi partition generator. The m-files also have their own help documentation that you can view in Matlab. This code creates a bounding polygon that is a square. Then it randomly creates 10 points and then creates and draws the partition. bounds = [0, 10, 10, 0 0, 0, 10, 10] points = 10 * rand(2, 10) regions = voronoi(bounds, points) drawRegions(bounds, regions) ================================ This is a brief set of instructions on how to use the Multiplicatively- weighted Voronoi partition generator. Each of the m-files has a help section, so in Matlab you can call: help mwvoronoi help drawRegions Here is an example that creates a bounding polygon, a square with side length 10. Then it randomly creates six points with random weights. Finally, the code draws the resulting partition. bounds = [0, 10, 10, 0 0, 0, 10, 10] points = 10 * rand(3, 6) regions = mwvoronoi(bounds, points) drawRegions(bounds, regions) Enjoy!

用神经网络，实现手写数字0-9的识别，对于相似度小的拒绝识别(Using neural networks, implementation figure 0-9 handwriting recognition, the similarity of small refuse recognition)