Aide Matlab
Aide matlab
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je suis partiellement disponible pour vous aidé en Matlab!- Génié éléctrique -
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Neural Networks - DCT for Face Identification is a free utility that will minimize image information redundancy
Neural Networks - DCT for Face Identification is a free utility that will minimize image information redundancy to avoid inefficiencies. Matlab source code.
High information redundancy and correlation in face images result in inefficiencies when such images are used directly for recognition.
Discrete cosine transforms are used to reduce image information redundancy because only a subset of the transform coefficients are necessary to preserve the most important facial features such as hair outline, eyes and mouth.
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A simple and effective code for Face Recognition Based on Polar Frequency Features.
A novel biologically motivated face recognition algorithm based on polar frequency is presented. Polar frequency descriptors are extracted from face images by Fourier-Bessel transform (FBT). Most of the current face recognition algorithms are based on feature extraction from a Cartesian perspective, typical to most analog and digital imaging systems.
The primate visual system, on the other hand, is known to process visual stimuli logarithmically. An alternative representation of an image in the polar frequency domain is the two-dimensional Fourier-Bessel Transform. This transform found several applications in analyzing patterns in a circular domain, but was seldom exploited for image recognition.
These results indicate the high informative value of the polar frequency content of face images in relation to recognition and verification tasks, and that the Cartesian frequency content can complement information about the subjects’ identity, but possibly only when the images are not pre-normalized. Give Fourier-Bessel Transform for Face Recognition a try to see what it's really capable of!
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Open Genetic Algorithm Toolbox
Open Genetic Algorithm Toolbox was developed as an accessible MATLAB tool that allows you to run a Genetic Algorithms on any problem you want to model.
Genetic Algorithms are search heuristics developed to find solutions to any problem through bio-inspired heuristics.
Now, you can use this accessible toolbox to quickly find solutions to your problems.
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Wind Turbin Toolbox
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Installation Procedure for "Wind Turbine Blockset V3.0"
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Version for Matlab 7.0.0 / (Service Pack 1) & Simulink 5.1
Basically all the important models uses only Simulink blocks.
However some models (measurement models) uses blocks from "DSP Blockset".
Installation Procedure
1. save the WTB_v3.change file to your hard-disk;
2. change the extension from *.change to *.zip
3. unzip this file
4. copy the entire folder WTB_v3 somewhere on your hard-disk.
A good location is <matlab root>\toolbox\
5. launch Matlab
6. go to File-> SetPath..
7. press "Add Folder with subfolders" button
8. browse for the folder WTB_v3 and press "OK"
9. press "Save" button and then close
10. open Simulink and the toolbox should appear somewhere at the bottom of the Simulink Toolboxes list
Note: Keep the Matlab path for this folder in top of the path list.
% ------------------------------------------------------------------------------------------------
% MATLAB SCRIPT STATE-SPACE REPRESENTATION
% MATLAB SCRIPT STATE-SPACE REPRESENTATION
% --------------------------
clear;
clc;
% Values of the motor parametres
d = 0.199;
J = 0.022;
r = 1.9;
b = 0.49;
k = 0.017;
% Matrix A,B C, and D
a = [0 1 0; 0 -d / J k / J; 0 -k / 1 -r / 1];
b = [0; 0; 1 / 1];
c = [1 0 0];
d = 0;
% Output = teta rotation angle
dc_motor = ss (a, b, c, d)
figure(1)
bode(dc_motor),grid on
figure(2)
nichols(dc_motor),grid on
figure(3)
nyquist(dc_motor),grid on
figure(4)
rlocus(dc_motor),grid on
figure(5)
pzmap(dc_motor),grid on
figure(6)
step(dc_motor),grid on
figure(7)
impulse(dc_motor),grid on
% The system (a, b, c, d) is called "dc_motor"
% Calculation of FT (tf_dc_motor)
% as a quotient of 2 polynomials
tf_dc_motor = tf (dc_motor)
figure(8)
bode(tf_dc_motor),grid on
figure(9)
nichols(tf_dc_motor),grid on
figure(10)
nyquist(tf_dc_motor),grid on
figure(11)
rlocus(tf_dc_motor),grid on
figure(12)
pzmap(tf_dc_motor),grid on
figure(13)
step(tf_dc_motor),grid on
figure(14)
impulse(tf_dc_motor),grid on
%verification
figure(15)
bode(dc_motor,tf_dc_motor),grid on,legend(' ss','tf')
figure(16)
nichols(dc_motor,tf_dc_motor),grid on,legend(' ss','tf')
figure(17)
nyquist(dc_motor,tf_dc_motor),grid on,legend(' ss','tf')
figure(18)
rlocus(dc_motor,tf_dc_motor),grid on,legend(' ss','tf')
figure(19)
pzmap(dc_motor,tf_dc_motor),grid on,legend(' ss','tf')
figure(20)
step(dc_motor,tf_dc_motor),grid on,legend(' ss','tf')
figure(21)
impulse(dc_motor,tf_dc_motor),grid on,legend(' ss','tf')
%--------------------------------------------------------end
% -------------------------
clear;close all;clc;
num=1;den=[10 1]; TP3=tf(num, den);
%sys continu
figure(1)
bode(TP3), grid
%sys disc
Te=2;
TP3_D=c2d(TP3,Te);
figure(2)
bode(TP3_D), grid
%- Calcul dans Bode -----%
figure(3)
bode(TP3, TP3_D), grid
%effet du te sur bode
TP3_D1=c2d(TP3,1e-5);
TP3_D2=c2d(TP3,1e-4);
TP3_D3=c2d(TP3,1e-3);
TP3_D4=c2d(TP3,1e-2);
TP3_D5=c2d(TP3,1e-1);
TP3_D6=c2d(TP3,1);
TP3_D7=c2d(TP3,10);
TP3_D8=c2d(TP3,100);
figure(4)
bode(TP3_D1, TP3_D2, TP3_D3, TP3_D4, TP3_D5, TP3_D6, TP3_D7,TP3_D8), grid, legend('TP3_D1', 'TP3_D2', 'TP3_D3', 'TP3_D4', 'TP3_D5', 'TP3_D6', 'TP3_D7','TP3_D8')
%Nyquist -----%
%sys continu
figure(5)
nyquist(TP3), grid
%sys disc
figure(6)
nyquist(TP3_D), grid
%all
figure(7)
nyquist(TP3, TP3_D), grid
%%%%%%%%%%%
%effet du te sur nyquist
figure(8)
nyquist(TP3_D1, TP3_D2, TP3_D3, TP3_D4, TP3_D5, TP3_D6, TP3_D7,TP3_D8), grid ,legend('TP3_D1', 'TP3_D2', 'TP3_D3', 'TP3_D4', 'TP3_D5', 'TP3_D6', 'TP3_D7','TP3_D8')
%nichols
%sys continu
figure(9)
nichols(TP3); ngrid
%sys disc
figure(10)
nichols(TP3_D), grid
%all
figure(11)
nichols(TP3,TP3_D), grid
Date de dernière mise à jour : 01/05/2018
Commentaires
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- 1. Mellah Hacene Le 26/09/2014
Invalid or Deleted File.
use my gmail -
- 2. DAHMANI Nadia Le 07/03/2014
Salem monsieur,
Je vous contacte a propos de la modélisation d'une machine asynchrone j'ai implanté mon modèle sous simulink mais en faisant ma simulation j'ai pas vraiment obtenus les courbes habituelles, je m'approche de plus en plus de ces courbes mais il existe un problème quelque part que je n'arrive pas a le trouver, alors je vous demande si c'est possible de me vérifier mon modèle j'ai des doutes au niveau des expressions trigonométriques de Park directe et inverse je suis pas vraiment sur de mes expressions a chaque fois je changes la syntaxe les courbes changent donc voila et merci d'avance.
Pour mon modèle le voila : https://www.mediafire.com/?1ye1aua7w6vnj13
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