Artificial-Neural-Networks-The-Tutorial-With-MATL

matlab 7.0教程 清华大学出版社出版 介绍了matlab的基本功能(matlab 7.0 tutorial)

说明：  此算法为最小二乘辅助变量法。uy2为含噪声数据，n=2,噪声模型n=2。左为输出数据，右为输入数据。k=2,3,4,5等等(Least-squares algorithm for the auxiliary variables. uy2 for data containing noise, n = 2, the noise model n = 2. Output data for the left and right for the input data. k = 2,3,4,5, etc.)

该算法用于对信号进行频谱分析，以及噪声处理(The algorithm is used for spectrum analysis of the signal, and noise processing)

matlab采用512个频率点绘制各种窗函数的幅频特性(matlab using 512 frequency points, drawing all kinds of window function of the amplitude-frequency characteristics)

说明：  read an image then draw it s 3D Histogram,include R and G or R and B or G and B. I have never found correct code of this before

这个是永磁同步电机矢量控制系统仿真，仿真结果正确，可以在空载、加载下很好的跟踪转速给定值。(This is a permanent magnet synchronous motor vector control system simulation, simulation results are correct, you can no load, a very good track speed setpoint under load.)

拉丁超立方抽样，调用方式如下：S=lhs(m,dist,mu,sigma,lowb,upb) m: a scalar,the number of sample points dist: A row with distribution type flags of basic random variables the value of the flag can be 1 (for uniform distribution, 2(for normal distribution), 3(for lognormal) and 4(for extreme type 1). mu: A row vector comprising the mean value of basic random variables. sigma: A row vector with its length equaligng to mu,including the standard deviation of basic random variables. lowb: a row vector with its elements are the lower bound of the sampling interval upb:a row vector with its elements are the upper bounds of the sampling interval dist,mu,sigma,lowb,upb must have the same length. Output argument S: sampling point matrix, of which each row is a sampling point.(code of Latin Hypercube Sampling)

APPLICATION OF A SHUNT ACTIVE POWER FILTER TO COMPENSATE MULTIPLE NON-LINEAR LOADS

calculator with mplab c18

遗传算法是计算机科学人工智能领域中用于解决最优化的一种搜索启发式算法，是进化算法的一种。这种启发式通常用来生成有用的解决方案来优化和搜索问题。进化算法最初是借鉴了进化生物学中的一些现象而发展起来的，这些现象包括遗传、突变、自然选择以及杂交等。(In the field of artificial intelligence, a genetic algorithm (GA) is a search heuristic that mimics the process of natural selection. This heuristic (also sometimes called a metaheuristic) is routinely used to generate useful solutions to optimization and search problems.[1] Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover.)