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trainFcn_2nd ='trainlm'; net_2nd=feedforwardnet(number_hidden,trainFcn_2nd); net_2nd=configure(net_2nd,inputs,targets);

[a, b]=min(ybest);

xo=xbest(b,:); k=0;

% Setup global best for weight and bias in neural network

% inputlayers*hiddenlayers xi=zeros(number_hidden,number_input); for i=1:number_hidden

for j=1:number_input k=k+1; xi(i,j)=xo(k);

end end

% outputlayers*hiddenlayers xl=zeros(number_output,number_hidden); for i=1:number_hidden

for j=1:number_output k=k+1; xl(j,i)=xo(k);

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end end

% hiddenlayers xb1=zeros(number_hidden,1); for i=1:number_hidden

k=k+1; xb1(i,1)=xo(k);

end

% outputlayers xb2=zeros(number_output,1); for i=1:number_output

k=k+1;

xb2(i,1)=xo(k); end

% Chose your train function in ANN trainFcn ='trainlm';

net_2nd = feedforwardnet(number_hidden,trainFcn); net_2nd = configure(net,inputs,targets);

net_2nd = fitnet(number_hidden,trainFcn);

% Close train window net_2nd.trainParam.showWindow = 1;

% Train

[net_2nd] = train(net_2nd,inputs,targets); outputs_2=net_2nd(inputs); error_2=outputs_2 - targets;

err_2nd = mean(error_2.^2)/mean(var(targets',1)) x=inputs;

t = targets;

y = net_2nd(x);

perf3 = perform(net_2nd,t,y)

% This line will caculate for 2 outputs err_2nd=norm(err_2nd);

disp (['the differen between output and target is: ' num2str(err_2nd)]) disp (['Epoch --------------------------- ' num2str(epoch)])

if err_2nd < err_1st

disp ('complete model')

break %Terminate execution of for or while loop else

weight_bias = getwb(net_2nd); end

end

disp('Complete Final model net_f') toc;

%% Test

disp('Testing out put....')

% Test for all value in used data Test_ouputs1=net_2nd(inputs); Test_ouputs1 = [Test_ouputs1; targets]; nntraintool('plot','plotregression')

set(findall(gcf,'-property','FontSize'),'FontSize',10) for i1 = 1:4

subplot(2,2,i1) grid on

end nntraintool('plot','plotperform')

set(findall(gcf,'-property','FontSize'),'FontSize',10) grid on;

nntraintool('plot','plottrainstate')

set(findall(gcf,'-property','FontSize'),'FontSize',10) grid on;

nntraintool('plot','ploterrhist')

set(findall(gcf,'-property','FontSize'),'FontSize',10) grid on;

timeElapsed = toc

Dầm trong phòng thí nghiệm

clear all;

% materials

E = 1.96E11; poisson = 0.30;

% E = 1.82095E11; poisson = 0.30;

% matriz C

C=E/(1-poisson^2)*[1 poisson 0;poisson 1 0;0 0 (1-poisson)/2];

% % load

% P = 1e6;

rho=7810; thickness=15e-3;

% thickness=6e-3;

%Mesh generation Lx=800e-3;

Ly=50e-3;

% Lx=0.6; % hauteur de la section droite de la poutre

% Ly=38e-3; % largeur de la section droite de la poutre L=Lx;

B=Ly;

numberElementsX=101; numberElementsY=1; Nx=numberElementsX; Ny=numberElementsY;

numberElements=numberElementsX*numberElementsY; [nodeCoordinates, elementNodes] = MeshRectangular(L,B,Nx,Ny);

% rectangularMesh(Lx,Ly,numberElementsX,numberElementsY); xx=nodeCoordinates(:,1);

yy=nodeCoordinates(:,2);

% drawingMesh(nodeCoordinates,elementNodes,'Q4','k-'); numberNodes=size(xx,1);

% GDof: global number of degrees of freedom GDof=2*numberNodes; [stiffness,mass]=formStiffness2D(GDof,numberElements,...

elementNodes,numberNodes,nodeCoordinates,C,rho,thickness); [Val_prop_Range,Indice] = sort(diag(Val_prop)); % définition de l'ordre des indices Vect_prop_range = Vect_prop(:,Indice); % Matrice ordonnée (Vecteurs propres) K_range=stiffness;

M_range=mass;

Puls_prop = Val_prop_Range.^(1/2) ;% Pulsation propre (en rad/s) freqHz = Puls_prop/(2*pi) ; % UNITS : Hertz freqHzz=freqHz(4:10,:)

Cầu Bến Quan

clc; close all;

% bvib manual

% Tutorial: static analysis

% Units: m, kN

% Nodes=[NodID X Y Z] lspan = 12;

%dy = 0.0001; %m, distance between nodes at a truss joint b=0;

% reference node

% Check the node coordinates as follows: figure

plotnodes(Nodes);

Element types -> {EltTypID EltName}

% Sections=[SecID A ky kz Ixx Iyy Izz yt yb zt zb] Sections=[1 A inf inf 0 0 Izz yt yb b/2 b/2];

% 2 pi*r^2 NaN NaN NaN NaN NaN NaN NaN NaN NaN];

% Materials=[MatID E nu]; E=2E11;

Materials= [1 E 0.3 7850] Code=1;

% Elements=[EltID(ten phan tu) TypID SecID MatID n1 n2 n3)] Elements=Elements_Sources_beam_Benquan(Code);

figure; plotnodes(Nodes,'numbering','off'); hold('on')

plotelem(Nodes,Elements,Types,'numbering','on'); DOF=getdof(Elements,Types);

seldof = [0.01;0.04 [Edge4]+0.02 [Edge4]+0.03 [Edge4]+0.05

[Edge1;Edge3]+0.05 Edge2+0.03 Edge2+0.05];

DOF=removedof(DOF,seldof); [K,M]=asmkm(Nodes,Elements,Types,Sections,Materials,DOF);

% bvib manual

% Tutorial: dynamic analysis: eigenvalue problem

% Units: m, N

% Assembly of M and K

% Eigenvalue problem nMode=20; [phi,omega]=eigfem(K,M,nMode);

% Display eigenfrequenties

% disp('Lowest eigenfrequencies [Hz]'); a=omega/2/pi;

idx=[1 2 3 4];

figure; plotdisp(Nodes,Elements,Types,DOF,phi(:,1),'DispMax','off') figure; plotdisp(Nodes,Elements,Types,DOF,phi(:,2),'DispMax','off') figure; plotdisp(Nodes,Elements,Types,DOF,phi(:,3),'DispMax','off') figure; plotdisp(Nodes,Elements,Types,DOF,phi(:,4),'DispMax','off') fre_FEM=a(idx)