lab manual soft computing

7/26/2019 Lab Manual Soft Computing

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One Week Faculty Development Program on

Artificial Intelligence and Soft Computing Techniques

Under TEQIP Phase II

(28th

April 02nd

May 2015)

LAB MANUAL

Organized by

Department of Computer Science & Engineering

University Institute of Technology

Rajiv Gandhi Proudyogiki Vishwavidhyalaya

(State Technological University of Madhya Pradesh)

Airport Road, Bhopal 462033

Website: www.uitrgpv.ac.in


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SNO. LIST OF EXPERIMENTS PAGE NO.

1. WAP to implement Artificial Neural Network 2

2. WAP to implement Activation Functions 3

3. WAP to implement Adaptive prediction in ADALINE NN 5

4. WAP to implement LMS and Perceptron Learning Rule 7

5. WAP to implement ART NN 12

6. WAP to implement BAM Network 14

7. WAP to implement Full CPN with input pair 15

8. WAP to implement discrete Hopfield Network 17

9. WAP to implement Hebb Network 18

10. WAP to implement Hetro associate neural net for mapping input vectors

to output vectors

19

11. WAP to implement Delta Learning Rule 20

12. WAP to implement XOR function in MADALINE NN 22

13. WAP to implement AND function in Perceptron NN 24

14. WAP to implement Perceptron Network 26

15. WAP to implement Feed Forward Network 32

16. WAP to implement Instar learning Rule 38

17. WAP to implement Weight vector Matrix 43

INDEX


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Experiment No. 1

AIM: WAP to implement Artificial Neural Network in MATLABCODE:

%Autoassociative net to store the vector

clc;clear;x=[1 1 -1 -1];

w=zeros(4,4);

w=x'*x;

yin=x*w;

for i=1:4

if yin(i)>0y(i)=1;

elsey(i)=-1;

end

end

disp('wieght matrix');

disp(w);

if x==y

disp('The vector is a known vector');

else

disp('The vector is a unknown vector');end

OUTPUT:

Weight matrix

1 1 -1 -1

1 1 -1 -1

-1 -1 1 1

-1 -1 1 1

The vector is a known vector


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Experiment No. 2

AIM: WAP to implement Activation Function in MATLAB

CODE:

>> % Illustration of various activation functions used in NN's

x=-10:0.1:10;

tmp=exp(-x);

y1=1./(1+tmp);

y2=(1-tmp)./(1+tmp);

y3=x;

subplot(231); plot(x,y1); grid on;

axis([min(x) max(x) -2 2]);

title('Logistic Function');

xlabel('(a)');

axis('square')

subplot(232);plot(x,y2); grid on;

axis([min(x) max(x) -2 2]);

title('Hyperbolic Tangent Function');qw

xlabel('(b)');

axis('square');

subplot(233);plot(x,y3); grid on;

axis([min(x) max(x) min(x) max(x)]);

title('Identity Function');

xlabel('(c)');

axis('square');


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OUTPUT:

-10 0 10-2

-1

0

1

2Logistic Function

(a)

-10 0 10-2

-1

0

1

2Hyperbolic Tangent Function

(b)

-10 0 10-10

-5

0

5

10Identity Function

(c)


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Experiment No. 3

AIM: WAP to implement Adaptive Prediction in ADALINE Network

CODE:

% Adaptive Prediction with Adaline

clear;

clc;

%Input signal x(t)

f1=2; %kHzts=1/(40*f1); % 12.5 usec -- sampling time

N=100;

t1=(0:N)*4*ts;

t2=(0:2*N)*ts+4*(N+1)*ts;t=[t1 t2]; %0 to 7.5 sec

N=size(t,2); % N = 302

xt=[sin(2*pi*f1*t1) sin(2*pi*2*f1*t2)];plot(t, xt), grid, title('Signal to be predicted')

p=4; % Number of synapses% formation of the input matrix X of size p by N

%use the convolution matrix. Try convmtx(1:8, 5)

X = convmtx(xt, p) ; X=X(:,1:N);

d=xt; % The target signal is equal to the input signal

y=zeros(size(d)); % memory allocation for y

eps=zeros(size(d)); % memory allocation for epseta=0.4 ; %learning rate/gainw=rand(1, p) ; % Initialisation of weight vector

for n=1:N % learning loopy(n)=w*X(:,n); %predicted output signal

eps(n)=d(n)-y(n); %error signal

w=w+eta*eps(n)*X(:,n)';

end

figure(1)

plot(t, d, 'b',t,y, '-r'), grid, ...title('target and predicted signals'), xlabel('time[sec]')

figure(2)plot(t, eps), grid, title('prediction error'), xlabel('time[sec]')


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OUTPUT:

0 1 2 3 4 5 6 7 8-1.5

-1

-0.5

0

0.5

1

1.5

2 target and predicted signals

time[sec]

0 1 2 3 4 5 6 7 8-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4prediction error

time[sec]


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Experiment No. 4

AIM: WAP to implement LMS and Perceptron Learning rule

CODE:

%For the following 2-class problem determine the decision boundaries

%obtained by LMS and perceptron learning laws.

% Class C1 : [-2 2]', [-2 3]', [-1 1]', [-1 4]', [0 0]', [0 1]', [0 2]',% [0 3]' and [1 1]'

% Class C2 : [ 1 0]', [2 1]', [3 -1]', [3 1]', [3 2]', [4 -2]', [4 1]',% [5 -1]' and [5 0]'

clear;

inp=[-2 -2 -1 -1 0 0 0 0 1 1 2 3 3 3 4 4 5 5;2 3 1 4 0 1 2 3 1 0 1 -1 1 2 -2 1 -1 0];

out=[1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0];

choice=input('1: Perceptron Learning Law\n2: LMS Learning Law\n Enter your choice :');

switch choice

case 1network=newp([-2 5;-2 4],1);

network=init(network);

y=sim(network,inp);

figure,plot(inp,out,inp,y,'o'),title('Before Training');

axis([-10 20 -2.0 2.0]);network.trainParam.epochs = 20;

network=train(network,inp,out);

y=sim(network,inp);

figure,plot(inp,out,inp,y,'o'),title('After Training');axis([-10 20 -2.0 2.0]);

display('Final weight vector and bias values : \n');

Weights=network.iw{1};

Bias=network.b{1};

Weights

Bias

Actual_Desired=[y' out'];Actual_Desiredcase 2

network=newlin([-2 5;-2 4],1);

network=init(network);y=sim(network,inp);

network=adapt(network,inp,out);

y=sim(network,inp);

display('Final weight vector and bias values : \n');

Weights=network.iw{1};Bias=network.b{1};


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Weights

BiasActual_Desired=[y' out'];

Actual_Desiredotherwise

error('Wrong Choice');

end

OUTPUT:

1: Perceptron Learning Law

2: LMS Learning Law

Enter your choice :1

Final weight vector and bias values : \n

Weights =

-1 1

Bias =

0

Actual_Desired =

1 1


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1 1

1 1

1 1

1 1

1 1

1 1

1 1

1 1

0 0

0 0

0 0

0 0

0 0

0 0

0 0

0 0

0 0


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-10 -5 0 5 10 15 20-2

-1.5

-1

-0.5

0

0.5

1

1.5

2Before Training

-10 -5 0 5 10 15 20-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

After Training


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0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

10-0.5

10-0.4

10-0.3

10-0.2

10-0.1

10

0

Best Training Performance is 0.5 at epoch 0

MeanAbsoluteError(mae)

2 Epochs

Train

Best


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Experiment No. 5

AIM: WAP to implement ART Neural Network

CODE:

%ART Neural Net

clc;

clear;

b=[0.57 0.0 0.3;0.0 0.0 0.3;0.0 0.57 0.3;0.0 0.47 0.3];

t=[1 1 0 0;1 0 0 1;1 1 1 1];vp=0.4;

L=2;

x=[1 0 1 1];

s=x;ns=sum(s);

y=x*b;

con=1;while con

for i=1:3if y(i)==max(y)

J=i;

end

end

x=s.*t(J,:);

nx=sum(x);if nx/ns >= vp

b(:,J)=L*x(:)/(L-1+nx);

t(J,:)=x(1,:);con=0;

else

y(J)=-1;

con=1;

end

if y+1==0con=0;

endend

disp('Top Down Weights');

disp(t);

disp('Bottom up Weights');

disp(b);


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OUTPUT:

Top Down Weights

1 1 0 0

1 0 0 1

1 1 1 1

Bottom up Weights

0.5700 0.6667 0.3000

0 0 0.3000

0 0 0.3000

0 0.6667 0.3000


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Experiment No. 6

AIM: WAP to implement BAM Network

CODE:

%Bidirectional Associative Memory neural net

clc;

clear;

s=[1 1 0;1 0 1];

t=[1 0;0 1];x=2*s-1

y=2*t-1

w=zeros(3,2);

for i=1:2w=w+x(i,:)'*y(i,:);

end

disp('the calculated weight matrix');disp(w);

OUTPUT:

x =

1 1 -1

1 -1 1

y =

1 -1

-1 1

the calculated weight matrix

0 0

2 -2

-2 2


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Experiment No. 7

AIM: WAP to implement Full Counter Propagation Network with input pair

CODE:

%Full Counter Propagation Network for given input pair

clc;

clear;

%set initial weights

v=[0.6 0.2;0.6 0.2;0.2 0.6;0.2 0.6];w=[0.4 0.3;0.4 0.3;];

x=[0 1 1 0];

y=[1 0];

alpha=0.3;for j=1:2

D(j)=0;

for i=1:4D(j)=D(j)+(x(i)-v(i,j))^2;

endfor k=1:2

D(j)=D(j)+(y(k)-w(k,j))^2;

end

end

for j=1:2

if D(j)==min(D)J=j;

end

enddisp('After one step the weight matrix are');

v(:,J)=v(:,J)+alpha*(x'-v(:,J))

w(:,J)=w(:,J)+alpha*(y'-w(:,J))


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OUTPUT:

After one step the weight matrix are

v =

0.4200 0.2000

0.7200 0.2000

0.4400 0.6000

0.1400 0.6000

w =

0.5800 0.3000

0.2800 0.3000


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Experiment No. 8

AIM: WAP to implement Discrete Hopfield Network

CODE:

% discrete hopfield net

clc;

clear;

x=[1 1 1 0];

tx=[0 0 1 0];w=(2*x'-1)*(2*x-1);

for i=1:4

w(i,i)=0;

endcon=1;

y=[0 0 1 0]

while conup=[4 2 1 3];

for i= 1:4yin(up(i))=tx(up(i))+y*w(1:4,up(i));

if yin(up(i))>0

y(up(i))=1;

end

end

if y==xdisp('convergence has been obtained');disp('the convergence output');

disp(y);con=0;

end

end

OUTPUT:

y =

0 0 1 0

convergence has been obtained

the convergence output

1 1 1 0


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Experiment No. 9

AIM: WAP to implement Hebb Network

CODE:

%Hebb Net to classify two dimensional inputs patterns

clear;

clc;

%Input Patterns

E=[1 1 1 1 1 -1 -1 -1 1 1 1 1 1 -1 -1 -1 1 1 1 1];F=[1 1 1 1 1 -1 -1 -1 1 1 1 1 1 -1 -1 -1 1 -1 -1 -1];

x(1,1:20)=E;

x(2,1:20)=F;

w(1:20)=0;t=[1 -1];

b=0;

for i=1:2w=w+x(i,1:20)*t(i);

b=b+t(i);end

disp('Weight matrix');

disp(w);

disp('Bias');

disp(b);

OUTPUT:

Weight matrix

Columns 1 through 12

0 0 0 0 0 0 0 0 0 0 0 0

Columns 13 through 20

0 0 0 0 0 2 2 2

Bias

0


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Experiment No.10

AIM: WAP to implement Hetro associate neural net for mapping input vectors to output

vectors

CODE:

%Hetro associate neural net for mapping input vectors to output vectorsclc;

clear;x=[1 1 0 0;1 0 1 0;1 1 1 0;0 1 1 0];

t=[1 0;1 0;0 1;0 1];

w=zeros(4,2);

for i=1:4

w=w+x(i,1:4)'*t(i,1:2);end

disp('weight matrix');

disp(w);

OUTPUT:

weight matrix

2 1

1 2

1 2

0 0


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Experiment No. 11

AIM: WAP to implement Delta Learning Rule

CODE:

% Determine the weights of a network with 4 input and 2 output units using

% Delta Learning Law with f(x)=1/(1+exp(-x)) for the following input-output

% pairs:

%

% Input: [1100]' [1001]' [0011]' [0110]'% output: [11]' [10]' [01]' [00]'

% Discuss your results for different choices of the learning rate parameters.

% Use suitable values for the initial weights.

in=[1 1 0 0 -1;1 0 0 1 -1; 0 0 1 1 -1; 0 1 1 0 -1];

out=[1 1; 1 0; 0 1; 0 0];

eta=input('Enter the learning rate value = ');it=input('Enter the number of iterations required = ');

wgt=input('Enter the weights,2 by 5 matrix(including weight for bias):\n');for x=1:it

for i=1:4

s1=0;

s2=0;

for j=1:5

s1=s1+in(i,j)*wgt(1,j);s2=s2+in(i,j)*wgt(2,j);

end

wi=eta*(out(i,1)-logsig(s1))*dlogsig(s1,logsig(s1))*in(i,:);wgt(1,:)=wgt(1,:)+wi;

wi=eta*(out(i,2)-logsig(s2))*dlogsig(s2,logsig(s2))*in(i,:);

wgt(2,:)=wgt(2,:)+wi;

end

end

wgt


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OUTPUT:

Enter the learning rate value = 0.6

Enter the number of iterations required = 1

Enter the weights,2 by 5 matrix(including weight for bias):

[1 2 1 3 1;1 0 1 0 2]

wgt =

1.0088 1.9508 0.9177 2.9757 1.0736

1.0476 0.0418 1.0420 0.0478 1.9104


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Experiment No. 12

AIM: WAP to implement XOR function for MADALINE NN

CODE:

%Madaline for XOR function

clc;

clear;

%Input and Target

x=[1 1 -1 -1;1 -1 1 -1];t=[-1 1 1 -1];

%Assume initial weight matrix and bias

w=[0.05 0.1;0.2 0.2];

b1=[0.3 0.15];v=[0.5 0.5];

b2=0.5;

con=1;alpha=0.5;

epoch=0;while con

con=0;

for i=1:4

for j=1:2

zin(j)=b1(j)+x(1,i)+x(1,i)*w(1,j)+x(2,i)*w(2,j);

if zin(j)>=0z(j)=1;

else

z(j)=-1;end

end

yin=b2+z(1)*v(1)+z(2)*v(2);

if yin>=0

y=1;

elsey=-1;

endif y~=t(i)

con=1;

if t(i)==1

if abs(zin(1))>abs(zin(2))

k=2;

else

k=1;end

b1(k)=b1(k)+alpha*(1-zin(k));

w(1:2,k)=w(1:2,k)+alpha*(1-zin(k))*x(1:2,i);else


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for k=1:2

if zin(k)>0;b1(k)=b1(k)+alpha*(-1-zin(k));

w(1:2,k)=w(1:2,k)+alpha*(-1-zin(k))*x(1:2,i);end

end

end

end

end

epoch=epoch+1;

end

disp('weight matrix of hidden layer');disp(w);

disp('Bias of hidden layer');

disp(b1);disp('Total Epoch');

disp(epoch);

OUTPUT:

weight matrix of hidden layer

0.2812 -2.1031

-0.6937 0.9719

Bias of hidden layer

-1.3562 -1.6406

Total Epoch

3


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Experiment No. 13

AIM: WAP to implement AND function in Perceptron NN

CODE:

%Perceptron for AND function

clear;

clc;

x=[1 1 -1 -1;1 -1 1 -1];

t=[1 -1 -1 -1];w=[0 0];

b=0;

alpha=input('Enter Learning rate=');

theta=input('Enter Threshold value');con=1;

epoch=0;

while concon=0;

for i=1:4yin=b+x(1,i)*w(1)+x(2,i)*w(2);

if yin>theta;

y=1;

end

if yin=-theta

y=0;endif yin


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OUTPUT:

Enter Learning rate=0.6

Enter Threshold value0.8

Perceptron for AND function

Final weight matrix

1.2000 1.2000

Final Bias

-1.2000


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Experiment No. 14

AIM: WAP to implement Perceptron Network

CODE:

clear;

clc;

p1=[1 1]';p2=[1 2]';

p3=[-2 -1]';p4=[2 -2]';

p5=[-1 2]';p6=[-2 -1]';p7=[-1 -1]';p8=[-2 -2]';

% define the input matrix , which is also a target matrix for auto

% association

P=[p1 p2 p3 p4 p5 p6 p7 p8];%we will initialize the network to zero initial weights

net= newlin([min(min(P)) max(max(P)); min(min(P)) max(max(P))],2);

weights = net.iw{1,1}%set training goal (zero error)

net.trainParam.goal=0.0;%number of epochs

net.trainParam.epochs=400;

[net, tr]= train(net,P,P);

%target matrix T=P

%default training function is Widrow-Hoff Learning for newlin defined

%weights and bias after the trainingW=net.iw{1,1}B=net.b{1}

Y=sim(net,P);%Haming like distance criterion

criterion=sum(sum(abs(P-Y)')')

%calculate and plot the errors

rs=Y-P; legend(['criterion=' num2str(criterion)])

figure

plot(rs(1,:),rs(2,:),'k*')test=P+rand(size(P))/10;

%let's add some noise in the input and test the networkagainYtest=sim(net,test);

criteriontest=sum(sum(abs(P-Ytest)')')

figure

output=Ytest-P

%plot errors in the output

plot(output(1,:),output(2,:),'k*')


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OUTPUT:

weights =

0 0

0 0

W =

1.0000 -0.0000

-0.0000 1.0000

B =

1.0e-12 *

-0.1682

-0.0100

criterion =

1.2085e-12

Warning: Plot empty.

> In legend at 287

criteriontest =

0.9751

output =

Columns 1 through 7

0.0815 0.0127 0.0632 0.0278 0.0958 0.0158 0.0957

0.0906 0.0913 0.0098 0.0547 0.0965 0.0971 0.0485

Column 8

0.0800

0.0142


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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

-3 -2.5 -2 -1.5 -1 -0.5

x 10-13

-16

-14

-12

-10

-8

-6

-4

-2x 10

-15


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0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.1

0 50 100 150 200 250 300 350 400

10-25

10-20

10-15

10-10

10-5

100

Best Training Performance is 1.3086e-26 at epoch 400

MeanSquaredError(mse)

400 Epochs

Train

Best


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0 50 100 150 200 250 300 350 400-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

valfail

400 Epochs

Validation Checks = 0, at epoch 400


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10 20 30 40 50

10

20

30

40

50

Target

O

utput~=0.9

5*Target+

1.1

Training: R=0.97897

Data

Fit

Y = T

10 20 30 40 50

10

20

30

40

50

Target

O

utput~=0.9

1*Target+

1.4

Validation: R=0.92226

Data

Fit

Y = T

10 20 30 40 50

10

20

30

40

50

Target

O

utput~=0.9

1*Target+

1.5

Test: R=0.90828

Data

Fit

Y = T

10 20 30 40 50

10

20

30

40

50

Target

O

utput~=0.9

4*Target+

1.1

All: R=0.96404

Data

Fit

Y = T


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Experiment No.15

AIM: WAP to implement feed forward network

CODE:

% a)Design and Train a feedforward network for the following problem:

% Parity: Consider a 4-input and 1-output problem, where the output should be

% 'one' if there are odd number of 1s in the input pattern and 'zero'

% other-wise.

clear

inp=[0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1;0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1;...

0 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1;0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1];

out=[0 1 1 0 1 0 0 1 1 0 0 1 0 1 1 0];network=newff([0 1;0 1; 0 1; 0 1],[6 1],{'logsig','logsig'});

network=init(network);

y=sim(network,inp);figure,plot(inp,out,inp,y,'o'),title('Before Training');

axis([-5 5 -2.0 2.0]);network.trainParam.epochs = 500;

network=train(network,inp,out);

y=sim(network,inp);

figure,plot(inp,out,inp,y,'o'),title('After Training');

axis([-5 5 -2.0 2.0]);

Layer1_Weights=network.iw{1};Layer1_Bias=network.b{1};Layer2_Weights=network.lw{2};

Layer2_Bias=network.b{2};Layer1_Weights

Layer1_Bias

Layer2_Weights

Layer2_Bias

Actual_Desired=[y' out'];

Actual_Desired

OUTPUT:

Layer1_Weights =

1.0765 2.1119 2.6920 2.3388

-10.4592 -10.9392 10.0824 10.9071

6.0739 9.4600 -5.3666 -5.9492


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-6.0494 -18.5892 -5.9393 5.6923

-2.5863 -1.7445 -11.6903 3.7168

10.7251 -10.5659 9.8250 -10.4745

Layer1_Bias =

-16.0634

5.4848

9.5144

9.6231

7.4340

5.7091

Layer2_Weights =

-2.5967 -23.3294 15.7618 23.4261 -22.5208 -23.3569

Layer2_Bias =

18.4268


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Actual_Desired =

0.0000 0

1.0000 1.0000

0.9999 1.0000

0.0000 0

1.0000 1.0000

0.0000 0

0.0000 0

0.9998 1.0000

1.0000 1.0000

0.0000 0

0.0000 0

0.9999 1.0000

0.0000 0

1.0000 1.0000

1.0000 1.0000

0.0000 0


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-5 -4 -3 -2 -1 0 1 2 3 4 5-2

-1.5

-1

-0.5

0

0.5

1

1.5

2Before Training

-5 -4 -3 -2 -1 0 1 2 3 4 5-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

After Training


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0 5 10 15 20 25

10-8

10-6

10-4

10-2

100

Best Training Performance is 3.3016e-09 at epoch 26

MeanS

quaredError(mse)

26 Epochs

TrainBest


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10-10

10-5

100

gradient

Gradient = 1.9837e-08, at epoch 26

10-20

10-10

100

mu

Mu = 1e-13, at epoch 26

0 5 10 15 20 25-1

0

1

valfail

26 Epochs

Validation Checks = 0, at epoch 26

0 0.2 0.4 0.6 0.8 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Target

Output~=1*Target+1.6e-05

Training: R=1

Data

Fit

Y = T


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Experiment No. 16

AIM: WAP to implement Instar learning Rule

CODE:

% Using the Instar learning law, group all the sixteen possible binary

% vectors of length 4 into four different groups. Use suitable values for

% the initial weights and for the learning rate parameter. Use a 4-unit

% input and 4-unit output network. Select random initial weights in the

% range [0,1]

in=[0 0 0 0;0 0 0 1;0 0 1 0;0 0 1 1;0 1 0 0;0 1 0 1;0 1 1 0;0 1 1 1;1 0 0 0;1 0 0 1;1 0 1 0;1 0 1 1;1

1 0 0;1 1 0 1;1 1 1 0;1 1 1 1];

wgt=[0.4 0.1 0.2 0.7; 0.9 0.7 0.4 0.7; 0.1 0.2 0.9 0.8 ; 0.5 0.6 0.7 0.6];eta=0.5;

it=3000;

for t=1:it

for i=1:16for j=1:4

w(j)=in(i,:)*wgt(j,:)';

end

[v c]=max(w);

wgt(c,:)=wgt(c,:)+eta*(in(i,:)-wgt(c,:));

k=power(wgt(c,:),2);f=sqrt(sum(k));wgt(c,:)=wgt(c,:)/f;

endend

for i=1:16

for j=1:4

w(j)=in(i,:)*wgt(j,:)';

end

[v c]=max(w);if(v==0)

c=4;end

s=['Input= ' int2str(in(i,:)) ' Group= ' int2str(c)];

display(s);

end

wgt


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OUTPUT:

s =

Input= 0 0 0 0 Group= 4

s =


s =


s =


s =



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s =


s =


s =


s =


s =



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s =


s =


s =


s =


s =



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s =


wgt =

0.6548 0.4318 0.0000 0.6203

0.5646 0.6819 0.4651 0.0000

0.5646 0.0000 0.6819 0.4651

0.3877 0.5322 0.5322 0.5322


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Experiment No.17

AIM: WAP to implement weight vector matrix

CODE:

clc;

clear;

x= [-1 -1 -1 -1; -1 -1 1 1 ];

t= [1 1 1 1];

w= zeros(4,4);for i=1:2

w= w+x(i,1:4)'*x(i,1:4);

end

yin= t*w;for i= 1:4

if yin(i)>0

y(i)=1;else

y(i)= -1;end

end

disp('The Calculated Weight Matrix');

disp(w);

if x(1,1:4)==y(1:4)| x(2,1:4)==y(1:4)

disp('the vector is a known vector');else

disp('the vector is a unknown vector');

end

OUTPUT:

The Calculated Weight Matrix

2 2 0 0

2 2 0 0

0 0 2 2

0 0 2 2

the vector is a unknown vector

lab manual soft computing

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