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Table 2 Program algorithm

From: Optimization of underwater wet welding process parameters using neural network

Program algorithm

load matlab.mat

% inputs

I=DataProject(:,1);

D=DataProject(:,4);

U=DataProject(:,2);

H=DataProject(:,5);

v=DataProject(:,3);

F=[I U v D H];

%outputs

W=DataProject(:,6);

 

P=DataProject(:,7);

G=[W P R];

R=DataProject(:,8);

 

% training

 

p=F(1:12,:);

t=G(1:12,:);

% testing

 

x=F(13:16,:);

Z=[x y];

y=G(13:16,:);

 

% form the network

 

net=feedforwardnet([40],'trainscg');

net.trainParam.max_fail=2000;

net.trainParam.goal=0; % error goal

net.trainParam.lr=0.001;

net.trainParam.epochs=3000; % maximum iterations

net.trainParam.mc=0.9;

net.trainParam.show=25; % showing intervals

 

% Network initialization

 

net.initFcn='initlay';

[net,tr]=train(net,p',t'); % training the net

net.layers{1}.initFcn='initnw';

view(net)

net=init(net);% initialize the net (weights and biases initialized)

 

% simulating the network with training inputs for testing

 

f=net(x');

f'

% compare results/target

 

Error=f'-y

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