bitsprec

this matlab code about testing ofdm

exercise 4 for icarnegie ssd4

amplitude modulation system using simulink matlab

Function to perform Principle Component Analysis over a set of training vectors passed as a concatenated matrix. Usage:- [V,D,M] = pca(X,n) [V,D] = pca(X,aM,n) where:- <input> X = concatenated set of column vectors aM = assume that the mean is aM n = number of principal components to extract (optional) <output> V = ensemble of column eigen-vectors D = vector of eigen-values M = mean of X (optional)

matlab编写的点阵计算程序,用于阵列全息光镊的计算(spot array calculation)

Simulation of AR process

《MATLAB智能算法超级学习手册》随书光盘，《MATLAB智能算法超级学习手册》的内容主要包括马尔科夫链模型、层次分析法、粒子群算法、遗传算法、蚁群算法、鱼群算法、PID控制算法、神经网络算法等。智能算法对于很多初学者而言，有一定的困难，很难理解程序流程、数据的运算过程，因此给实际应用带来困难。本书将围绕智能算法展开综述，深入浅出地介绍和分析各类智能算法，用智能算法解决工程应用问题。(Content " MATLAB super intelligent algorithm to study the manual" CD with the book, " MATLAB super intelligent algorithm to learn Manual" includes Markov chain model, AHP, particle swarm optimization, genetic algorithm, ant colony algorithm, fish algorithm, PID algorithms, neural network algorithms and other controls. Intelligent Algorithm For many beginners, there are certain difficulties, it is difficult to understand the program flow, data calculation process, and therefore difficult to practice. The book will focus on intelligent algorithms were summarized, easy to introduce and analyze various types of smart algorithm, using intelligent algorithms to solve engineering problems.)

说明：  变窗宽模拟仿真的MATLAB程序 回归函数的估计mp,估计的偏差px,估计的方差vx(Variable window-width of the MATLAB simulation procedures for regression function estimation mp, the estimated bias px, the estimated variance vx)

PSO应用于PMU配置的简易实例,亲测之后感觉还不错(PSO applied to simple examples of PMU configuration , feeling pretty good after the pro-test )

支持向量机是用于分类与回归的技术。由于其自身的诸多缺点，如无法获得概率输出，需要估计一个误 差参数C，以及必须使用Mercer 核函数等。相关向量机算法，克服了SVM 上述缺点，RVM 能获得与SVM 相比拟的推 广性能，并且更为稀疏。在此基础上，文中介绍了一种RVM 回归用于分类的新分类方法，用RVRC 来表示。并通过 实验证明了它的可行性。(Support vector machines for classification and regression techniques. Due to its many shortcomings, such as the probability that the output can not be obtained, need to estimate an error parameter C, and the need to use Mercer kernel function and so on. Relevance vector machine algorithm to overcome these shortcomings of SVM, RVM and SVM can be obtained comparable to promote performance and more sparse. On this basis, the paper introduces a RVM regression was used to classify new classification method, RVRC to represent. And through experiments proved its feasibility.)