Matlabalgo

image segmentation using region based

POWER_FLOW Function to perform a power-flow analysis. Function power_flow reads a data set describing a power system from a disk file, and performs a power-flow analysis on it. The only argument is the name of the input file. There are three types of lines in the input file. The "SYSTEM" line specifies the name of the power system and the base apparent power of the power system in MVA.

MATLAB的一些经典使用案例 适合初学者掌握MATLAB的使用技巧(the use of MATLAB)

时域分析 频域分析程序 GUI设计 数据是台架振动数据 (pretreatment of vibration signal)

prima algoritm net acvtive source code , equation

It is about modeling and simulation of an D.C motor with excitation series using matlab/simulink software.

function [bestsole,bestpop]=ga_scomplex(num,bounds,ps,pc,pm,b,dtc) % GA＿scomple是基于浮点编码的单纯形遗传算法主程序 % num定义种群数量 %　bounds定义参数取值范围，格式为[Var1min,Var1max;Var2min,Var2max;…….]; %ps选择概率 %pc交叉概率 %pm变异概率 %b　变异程度系数 %dtc　迭代次数 % $Date: 2005/5/25 $(funzione [bestsole,bestpop]=ga _scomplex (num,limites,ps,pc,pm,b,dc) % GA 655343;scomple;;;;;; % num % limits23450;[Var1min,Var1max;Var2min,Var22min;;;;;]; %ps %pc %pm %b _ %dtc _ % $Data: 2005 /5 /25 $)

These pages introduce a MATLAB Graphical User Interface (GUI) for control of several Blind Source Separation (BSS) algorithms. The GUI enables to work with input and output data, multiple signals plot and saving output variables to the base workspace of Matlab or to a file. The GUI also provides the Monte-Carlo Analysis to validate features of the algorithms ivolved.

用遗传算法优化参数的递归T-S模糊神经网络学习算法，值得一看。(Parameter optimization using genetic algorithm recursive TS fuzzy neural network learning algorithm, worth a visit.)

constructive role of noise in signal detection from parallel arrays of quantizers.(A noisy input signal is observed by means of a parallel array of one-bit threshold quantizers,in which all the quantizer outputs are added to produce the array output.s(t)=s1(t)with prior probability P0,or s(t)=s0(t)with prior probability P1=1-P0 input signal-noise mixture x(t)=s(t)+n(t) x(t) is observed by means of a parallel array of M threshold comparators. yi(t)=U[x(t)+wi(t)-theta],i=1,2...M.wi(t) is threshold noise.Y(t)=y1(t)+y2(t)+...yM(t), the array output Y(t) is meansured at T distinct times.the plot is fthe theoretical value of minimal detection error probability Per varied with sigma.)