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这是一个关于最小二乘支持向量机的MATLAB仿真的例子，希望能给大家带来方便。(This is a least squares support vector machine on the MATLAB simulation examples, we hope that they will be convenient.)

一个数字PID控制的matlab的m文件，希望能够与各位控制领域的朋友多多交流，以求共同进步！(A digital PID control matlab m-files, hoping to control the field with my friends a lot of exchanges, in order to make progress together!)

生成圆阵的DBF校正因子,该圆阵为环形阵列，生成数据已归一化(Generate circular array of DBF correction factor, the circular array of circular arrays to generate data have been normalized)

均匀线列阵加权波束形成，包括均匀加权，hanning, hamming, blackman和chebychev加权，并给出极坐标图。(Uniform linear array weighted beamforming, including the uniform weighted, hanning, hamming, blackman and chebychev weighted, and give the polar diagram.)

one of the most important areas for the application of Fuzzy Set Theory as developed by Zadeh in 1965 are Fuzzy Rule-Based Sys- tems (FRBSs).The ad hoc data-driven RB generation process proposed by Wang and Mendel in [14] has been widely known because of its simplicity and good performance.

Fingerprint Recognize System

排队论的最基本模型，MM1型。简单的小程序，注释清楚，方便理解。(The most basic model of queuing theory, MM1 type. Simple procedure, comments clear, easy to understand.)

Herewith i attached the matlab codes for Contingency analysis, Economic dispatch,Pv curve,Swing Curve, Ybus Inspection method and Zbus. I hope these are very useful.

编写卡尔曼滤波程序实现航迹的滤波估计，并计算出误差，仿真曲线(Kalman filter procedure to prepare implementation of the filtering track estimate and calculate the error, the simulation curve)

感知器感知器算法训练二元线性分类器。结构体数据使用感知器学习规则 找到给定的二分类标签数据超平面。 (PERCEPTRON Perceptron algorithm to train binary linear classifier. Synopsis: model = perceptron(data) model = perceptron(data,options) model = perceptron(data,options,init_model) Description: model = perceptron(data) uses the Perceptron learning rule to find separating hyperplane from given binary labeled data. model = perceptron(data,options) specifies stopping condition of the algorithm in structure options: .tmax [1x1]... maximal number of iterations. If tmax==-1 then it only returns index (model.last_update) of data vector which should be used by the algorithm for updating the linear rule in the next iteration. model = perceptron(data,options,init_model) specifies initial model which must contain: .W [dim x 1] ... normal vector. .b [1x1] ... bias of hyperplane. .t [1x1] (optional) ... iteration number. Input: data [struct] Labeled (binary) training data. .X [dim x num)