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Smith Nguyen Studio. Smith Nguyen eBooks. Smith Nguyen Studio. Smith Nguyen eBooks. Smith Nguyen Studio. Smith Nguyen eBooks.

The development of solar power generation, the most important thing is MPPT, or maximum power point tracking and grid inverter technology, this data analysis unique in English (2008-02-16,

说明：  蚂蚁所在的环境是一个虚拟的世界，其中有障碍物，有别的蚂蚁，还有信息素，信息素有两种，一种是找到食物的蚂蚁洒下的食物信息素，一种是找到窝的蚂蚁洒下的窝的信息素。每个蚂蚁都仅仅能感知它范围内的环境信息。环境以一定的速率让信息素消失。(The environment where ants are located is a virtual world, including obstacles, other ants, and pheromones. There are two kinds of information. One is food pheromone spilled by ants who find food, and the other is the pheromone of nest scattered by ants who find nest. Each ant can only perceive the environmental information in its scope. The environment makes pheromones disappear at a certain rate.)

this is the jpeg code i try to simulate in matlab

本程序为边界元的原程序，可用来求解2D位势问题，附数据文件(the procedures for the original boundary element procedures can be used to solve 2D Potential problems with data files)

三种信道 瑞利 赖斯 高斯的误码率仿真，通过MATLAB实现(The three channel Ruililaisi Gaussian error rate simulation)

一种改进型的PSO的机器学习算法实现，应用欧氏距离作为分类便准，KNN分类法实现分类(An improved PSO algorithm)

巴特沃斯低通滤波器、FIR低通滤波器、移动平均滤波器、中值滤波器、维纳滤波器、自适应滤波器和小波滤波器的比较(Butterworth low-pass filter, FIR low-pass filter, moving average filter, median filter, Wiener filter, adaptive filter and wavelet filter comparison)

逐步判别分析的主函数 用于分类 [sel,c,c0,re,P]=StepclaassV2(data,[50 50 50],data,10) 输入变量 x为训练集.每行为一个样本，每列为一个变量. Class_x,为训练集的分类情况，一行，为各类样本数量,例如[5 6 9] Test为待分类样本. 输出变量：sel为选择的变量序号，c,c0为拟合出的判别函数.re为对Test的判别结果,P为其后验概率. author 王新 2012.4.8 例子Sample: load fisheriris x=meas Class_x=[50,50,50] Test=meas(1:5,:) Fa1=4 [sel,c,c0,re,P]=StepclaassV2(x,Class_x,Test,Fa1)( Stepwise discriminant analysis for the classification of the main function [sel, c, c0, re, P] = StepclaassV2 (data, [50 50 50], data, 10) input variables x as the training set, each conduct a sample of each as a variable. Class_x, the classification for the training set, a row, the number of samples for all types, for example, [5 6 9] Test sample to be classified. output variable: sel for the selected variables serial number, c, c0 is fitted discriminant function. re discrimination results for the Test, P its posterior probability. author Wang 2012.4.8 examples Sample: load fisheriris x = meas Class_x = [50,50,50] Test = meas (1:5, :) Fa1 = 4 [sel, c, c0, re, P] = StepclaassV2 (x, Class_x, Test, Fa1))

详细介绍了自适滤波器的原理和实现，对于我们学习十分有帮助。(Adaptive filter described in detail the theory and implementation, very helpful for us to learn.)