Neural Network & GA Assignment

R.Bhalamurugan

1671210007

Tool Used: nntool

Literature: Analyzing Students’ Cognitive Load to Prioritize English Public Speaking Training

Data:

Using Command line programming with the Same Data % B_Input_N=[0.0948 0.0395 0.0352 0.0416 0.0273 0.0434 0.0312 0.0704 0.0447 0.0719 0.0743 0.1551 0.0732 0.032 0.0174 0.1299 0.0576 0.0885 0.0606 0.0371 0.2232 0.0184 0.0184 0.0834 0.1061 0.0282 0.0819 0.0241 0.0292;% 0.1292 0.0445 0.0399 0.1094 0.0273 0.0285 0.3531 0.0474 0.1192 0.0315 0.0381 0.3353 0.3001 0.0933 0.0174 0.0537 0.0254 0.0414 0.0273 0.0371 0.3704 0.0184 0.2587 0.0345 0.3463 0.0282 0.0372 0.0684 0.0672;% 0.2087 0.0395 0.0485 0.0416 0.0273 0.0259 0.1304 0.1853 0.0635 0.0936 0.0298 0.198 0.0934 0.1411 0.032 0.1299 0.0298 0.0885 0.0899 0.0371 0.1189 0.0355 0.0352 0.0401 0.2335 0.0282 0.042 0.0241 0.3761;% 0.3162 0.0395 0.0813 0.2528 0.0273 0.2252 0.0337 0.2751 0.0593 0.0324 0.1356 0.0354 0.032 0.0361 0.1377 0.1299 0.0468 0.1277 0.0348 0.0371 0.1189 0.1264 0.0309 0.0345 0.1421 0.0662 0.3789 0.0241 0.2266;% 0.055 0.0863 0.0674 0.0416 0.0704 0.0293 0.0207 0.0412 0.0411 0.048 0.0354 0.0314 0.0397 0.0825 0.2706 0.0537 0.0565 0.0324 0.0329 0.0865 0.0272 0.0624 0.229 0.0345 0.0519 0.0459 0.0216 0.3413 0.0769;

% 0.0743 0.1205 0.1068 0.0416 0.0704 0.0555 0.0388 0.0521 0.0368 0.4326 0.0487 0.0452 0.0989 0.3544 0.2706 0.0239 0.1474 0.0513 0.0393 0.0915 0.0272 0.0355 0.1953 0.0834 0.0304 0.107 0.0216 0.1871 0.1364;% 0.029 0.21 0.079 0.2528 0.4005 0.1177 0.0741 0.164 0.1222 0.0261 0.2477 0.0857 0.1192 0.0361 0.0458 0.4014 0.2899 0.0233 0.3652 0.1768 0.0272 0.1338 0.11 0.3653 0.0524 0.1606 0.0725 0.0916 0.0292;% 0.0443 0.21 0.3696 0.1094 0.1961 0.3568 0.2188 0.1015 0.3283 0.2307 0.3162 0.0466 0.0507 0.1925 0.1377 0.0537 0.1936 0.2006 0.1361 0.32 0.0598 0.3653 0.0687 0.182 0.0203 0.3839 0.2305 0.1871 0.0292;% 0.0486 0.21 0.1725 0.1094 0.1536 0.1177 0.0993 0.0628 0.1849 0.0333 0.0743 0.0672 0.1927 0.032 0.0706 0.0239 0.153 0.3463 0.2138 0.1768 0.0272 0.2042 0.0538 0.1426 0.017 0.1517 0.1139 0.0521 0.0292];% %Training Patterns (domain values)% % B_Target_N=[0.6485 1 0.677 0.926 0.4998 0.578 0.5666 0.919 0.7361 0.441 0.6865 0.3864 0.7723 0.9032 0.824 0.376 0.927 0.413 0.5314 0.7901 0 0.5414 0.8833 0.6167 0.3423 0.6218 0.3359 0.6291 0.5247% ; 19 31 20 29 8 14 13 28 22 7 21 5 23 27 25 4 30 6 11 24 1 12 26 15 3 16 2 18 9% ]; %Training Targets (range values)B_Input_N = xlsread('NNet.xlsx', 1);B_Target_N = xlsread('NNet.xlsx', 2); P=B_Input_N;T=B_Target_N;net = newff(minmax(P),[10 1],{'tansig' 'purelin'});%Plot the original data points and the untrained outputY = sim(net,P);figure(1);plot(P,Y,'o',P,T,'p');title('Data and Untrained Network Output');%Train the network and plot the resultsnet.trainParam.goal=0.01; %0 is the default- too small!net.trainParam.epochs = 50; %For our sample, don’t train too longnet.trainParam.lr = 0.001;net = train(net,P,T);X= xlsread('NNet.xlsx',3);% X=[0.064 0.0386;% 0.064 0.071;% 0.0474 0.0802;% 0.1333 0.0386;% 0.0329 0.3353;% 0.0329 0.0386;% 0.3781 0.1766;% 0.2301 0.1307;% 0.0173 0.0906];%New Domain Points Y = sim(net,X); %Network Outputfigure(2);% plot(P,T,'p');%hold on;plot (X,Y,'-o');title ('Output after trained');%hold off;%plot(P,T(1,:),X,Y(1,:));% hold on;% plot(P,T(2,:),X,Y(2,:));% hold off;

An alternative way to test training: postregfigure(3)Tout=sim(net,P); %Get network output for the training domain[m,b,r]=postreg(T,Tout); %Performs a linear regressiondisplay Y;

Out Put:

Genetic Algorithm:

Tool Used: gatool

Fitness Fuction:

function y = Bga(x) y = -(x^2-1);end

Initial Population:

function Pp=Pop(~)% Pp = [0.0948 0.0395 0.0352 0.0416 0.0273 0.0434 0.0312 0.0704 0.0447 0.0719 0.0743 0.1551 0.0732 0.032 0.0174 0.1299 0.0576 0.0885 0.0606 0.0371 0.2232 0.0184 0.0184 0.0834 0.1061];Pp=[0.0948 0.0395 0.0352 0.0416 0.0273 0.0434 0.0312 0.0704 0.0447 0.0719];end

Program :

function [x,fval,exitflag,output,population,score] = B_GA(nvars,PopulationSize_Data,CrossoverFraction_Data,Generations_Data,TolFun_Data,InitialPopulation_Data)

% Start with the default optionsoptions = gaoptimset;% Modify options settingoptions = gaoptimset(options,'PopulationType', 'custom');options = gaoptimset(options,'PopulationSize', PopulationSize_Data);options = gaoptimset(options,'CrossoverFraction', CrossoverFraction_Data);options = gaoptimset(options,'Generations', Generations_Data);options = gaoptimset(options,'TolFun', TolFun_Data);options = gaoptimset(options,'InitialPopulation', InitialPopulation_Data);options = gaoptimset(options,'CreationFcn', @Pop);options = gaoptimset(options,'FitnessScalingFcn', @fitscalingrank);options = gaoptimset(options,'CrossoverFcn', @crossovertwopoint);options = gaoptimset(options,'MutationFcn', @mutationadaptfeasible);options = gaoptimset(options,'Display', 'iter');options = gaoptimset(options,'PlotFcns', { @gaplotbestf @gaplotbestindiv @gaplotscores });options = gaoptimset(options,'OutputFcns', { [] });[x,fval,exitflag,output,population,score] = ...ga(@Bga,nvars,[],[],[],[],[],[],[],options);