power_switching_power_supply_lib

灰度图像灰度经过按位混沌加密后再对其混沌置乱变换，阀值判断其迭代次数，达到预期加密效果(After a bit gray gray chaotic encryption by scrambling after its chaotic transformation, determine the threshold number of iterations to achieve the desired effect of encryption)

problem of neuronal networks : perceptron multicouche

单纯形法用MATLAB实现的案例，方便运筹学的同学下载(Simplex method implemented with the MATLAB case, convenient download operations research students)

数字信号处理第十章 FIR滤波器的设计 代码实现(Digital Signal Processing Chapter FIR filter design code)

本资料为数字信号处理的一个课程设计源代码，内容关于数字带通滤波器设计。(this source code is about digital ban pass filter designing.)

说明：  清华大学电子系谷源涛老师MATLAB课件及演示程序，简明扼要。(Department of Electronics, Tsinghua University, Tao Gu source courseware and teacher MATLAB demonstration program, clear and concise.)

TMM模拟电磁波在堆膜中传播过程 和场分布,可简易改变参数 作不同体系的透射反射谱图和场分布图(TMM simulation of electromagnetic waves in the spread of membrane reactor process and field distribution can be easy to change the parameters for different systems of transmission reflection spectra and field distribution)

fit_maxwell_pdf - Non Linear Least Squares fit of the maxwellian distribution. given the samples of the histogram of the samples, finds the distribution parameter that fits the histogram samples. fits data to the probability of the form: p(r) = sqrt(2/pi)*(a^(-3/2))*(r^2)*exp(-(r^2)/(2*a)) with parameter: a format: result = fit_maxwell_pdf( x,y,W,hAx ) input: y - vector, samples of the histogram to be fitted x - vector, position of the samples of the histogram (i.e. y = f(x,a)) W - matrix or scalar, a square weighting matrix of the size NxN where N = length(y), or 0 to indicate no weighting is needed. hAx - handle of an axis, on which the fitted distribution is plotted if h is given empty, a figure is created. output: result - structure with the fields a - fitted parameter VAR - variance of the estimation type- weighted LS or not weighted LS iter- number of iteration for the solution

一个小的matlab程序利用RBFNN进行函数拟合(A small matlab program using RBFNN for function fitting)

聚类算法，不是分类算法。分类算法是给一个数据，然后判断这个数据属于已分好的类中的具体哪一类。聚类算法是给一大堆原始数据，然后通过算法将其中具有相似特征的数据聚为一类。这里的k-means聚类，是事先给出原始数据所含的类数，然后将含有相似特征的数据聚为一个类中。所有资料中还是Andrew Ng介绍的明白。首先给出原始数据{x1,x2,...,xn}，这些数据没有被标记的。初始化k个随机数据u1,u2,...,uk。这些xn和uk都是向量。根据下面两个公式迭代就能求出最终所有的u,这些u就是最终所有类的中心位置。(Clustering algorithm, not a classification algorithm. Classification algorithm is to give a figure, and then determine the data belonging to a specific class of good which category. Clustering algorithm is to give a lot of raw data, and then through the algorithm which has similar characteristics data together as a class. Here k-means clustering, is given in advance the number of classes contained in the raw data, then the data contain similar characteristics together as a class. All information presented in or Andrew Ng understand. Firstly, raw data {x1, x2, ..., xn}, the data is not labeled. K random initialization data u1, u2, ..., uk. These are the vectors xn and uk. According to the following two formulas can be obtained final iteration all u, u is the ultimate all these classes the center position.)