ARTIFICIAL NEURAL NETWORKS
[COMP 442)
LAB MANUAL
Department of Computer Science
College of Computer Science & Information Systems
Ministry of Higher Education |Jazan University
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Lab Course
Lab#
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Practical
Revision of C++ Programming concepts:
Program to show how to compute random numbers.
Program to compute the sigmoidal function.
Example showing how to compute the summation part of the neuron..
Threshold Function
Simulating the McCulloch & Pitts neurons for 2-input logical OR, and 2-input
logical AND.
Error Correction Learning Rule
Perceptron
Example on representing a line, and classifying points if they are above or
below the line.
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Classify the perceptron
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Implement the learning algorithm.
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Initialize randomly input signals with synaptic weight and compute the
summation part of the neurons and apply MLPs Logistic function.
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Initialize randomly input signals with synaptic weight and compute the
summation part of the neurons and apply MLPs Hyperbolic tangent function.
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//Lab #1: Write a program to compute random numbers.
#include<iostream>
#include<cmath>
#include <conio.h>
using namespace std;
void main()
{
int i, x[10], w[10];
cout<<"Display random numbers"<<endl<<endl;
for(i=0;i<10;i++)
{
x[i]= rand() % 5 + 1; //Gives a number between 1 and 5.
w[i]= rand() % 3 + 1; //Gives a number between 1 and 3.
cout<< "x[i] = " << x[i] <<"
w[i] = " << w[i] <<endl;
}
getch();
}
OUTPUT:
7
//Lab # 2: Write a program to compute the sigmoid function.
#include<iostream>
#include<cmath>
#include<conio.h>
using namespace std;
void main()
{
float j;
for(j=-1.0;j<=1.1;j=j+0.1)
{
cout<< "j = " <<j<<"
";
cout<<"Sigmoid Value = " <<1.0/(1+exp(-j)) <<endl;
}
getch();
}
OUTPUT:
8
//Lab # 3: Write a program to compute the summation of the neuron.
#include<iostream>
#include<conio.h>
#include<cmath>
using namespace std;
void main()
{
int i,
x[10], w[10], sum=0;
for(i=0;i<10;i++)
{
x[i]= rand() % 5 + 1; // Initialize the input signal randomly.
w[i]= rand() % 3 + 1;// Initialize the weights randomly.
sum = sum + x[i] * w[i];
}
cout<< "sum = " <<sum <<endl;
getch();
}
OUTPUT:
9
//Lab # 4: Write a program to simulating the McCulloch & Pitts neurons
//for 2-input logical OR, and 2-input logical AND.
//Apply Threshold Function
#include<iostream>
#include<conio.h>
#include<cmath>
using namespace std;
// Define the or function
int or (int i,int j)
{
int net;
int out;
const int w1 = 1;
const int w2 = 1;
const int theta = 0;
net = i * w1 +j * w2 - theta;
// The activation function is threshold (step) function.
if (net >= 0)
out=1;
else
out=0;
return out;
}
// Define the and function
int and (int i,int j)
{
int net;
int out;
const int w1 = 1;
const int w2 = 1;
const int theta = 1;
net = i * w1 +j * w2 - theta;
// The activation function is threshold (step) function.
if(net >= 0)
out=1;
else
out=0;
return out;
}
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void main()
{
int x1,x2,i;
cout<< "x2
x1
OR(x1,x2) AND(x1,x2) " <<endl;
cout<<"------------------------------------" <<endl;
for(x2 = 0; x2 < 2; x2++)
for(x1 = 0; x1 < 2; x1++)
cout<< x2 <<"
" << x1 <<"
";
cout<< or(x1,x2)<<"
"<< and(x1,x2) <<endl;
getch();
}
OUTPUT:
11
//Lab # 5: Write a program to find error signals
//Initialize randomly input signals with synaptic weights
//Initialize desired value.
//ek(n) = dk(n) – yk(n) [Apply “Error – Correction Learning Rule”]
#include<iostream>
#include<conio.h>
#include<time.h>
using namespace std;
void main()
{
system("color 5b");
int size;
cout<< "Enter number of Input signal : ";
cin>> size;
cout<< "--------------------\n";
double
double
double
double
double
x[100];
w[100];
bk = 1;
dk = 20; //desired value
yk=0; //actual output
srand(time(NULL));
// Random x and w
for(int i=0;i<size;i++)
{
x[i] = rand()% 5 - 5;
w[i] = rand() % 5 -5;
}
// print x and w
for(int i=0;i<size;i++)
{
cout<< "x"<<i + 1 << "=" << x[i] << "\t";
cout<< "w" << i+1 << "=" << w[i] <<endl;
}
cout<< "--------------------\n";
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//Compute actual output
for(int i=0;i<size;i++)
yk += x[i]*w[i];
yk += bk;
double ek;//Declare error signal
if(yk != dk)
ek = dk - yk; //Apply "Error Correction Learning Rule"
cout<< "The Actual result is (yk): " <<yk <<endl;
cout<< "ek(n) = dk(n) - yk(n)\nek(n) = " <<dk << " - " <<yk;
cout<< "\nek(n) = "<<ek <<endl;
getch();
}
OUTPUT:
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//Lab # 6: Write a program to representing a line
//Classifying points if they are above or belowthe line.
#include<iostream>
#include<conio.h>
using namespace std;
// Checks if the point (x,y) in 2-D is above or
//below the line: 3 X + 4 Y - 12 = 0.
int aboveline (int x,int y)
{
int net,out;
const int k = 1;
const int w0 = -12;
const int w1 = 3;
const int w2 = 4;
net = x * w1 + y * w2 + k * w0;
OUTPUT:
if (net >= 0)
out=1;
else
out=-1;
return out;
}
void main()
{
int x1,x2,i;
cout<< "Checks if the point (x1,x2) in 2-D is above " <<endl;
cout<< " or below the line: " <<endl;
cout<< " 3 X + 4 Y - 12 = 0. " <<endl;
cout<< "The program outputs: " <<endl;
cout<< " 1 if the point above the line" <<endl;
cout<< " -1 if the point below the line" <<endl;
cout<<"Enter the coordinates of the point: ";
cin>> x1;
cin>> x2;
cout<<endl;
cout<<"Display the results = "<<aboveline(x1,x2);
getch();
}
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//Lab # 7: Write a program to Classify the perceptron either in class
//C1 or C2 using decision boundary x1w1 + x2w2 + bk = 0
#include<iostream>
#include<conio.h>
using namespace std;
void main()
{
double x[2],w[2];
double bk;//bias
cout<<"Enter the value of bias= ";
cin>>bk;
for(int i=0;i<2;i++)
{
x[i]=rand()%11;
w[i]=rand()%11;
}
for(int i=0;i<2;i++)
{
cout<<"x"<<i+1<<"= "<<x[i]<<endl;
}
for(int i=0;i<2;i++)
{
cout<<"w"<<i+1<<"= "<<w[i]<<endl;
}
double v=0;
for(int i=0;i<2;i++)
{
v=v+w[i]*x[i];
}
v+=bk;
if(v>=0)
{
cout<<"result = "<<v<<endl;
cout<<"the result is within c1";
}
else
{
cout<<"result = "<<v<<endl;
cout<<"the result is within c2";
}
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cout<<endl;
getch();
}
OUTPUT:
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//Lab # 8.The perceptron learning algorithm.
//Training a perceptron to represent a line, so that we can classify
//points if they are above or below the line. Provide the function
//misclassified.Implement the learning algorithm.
#include<iostream>
#include<conio.h>
using namespace std;
int misclassified(int inputclass[],int outclass[])
{
int i;
int point=-1;
for(i=0; i< 5; i++)
if (inputclass[i] != outclass[i])
point=i;
return point;
}
void evaluate(int x[5][2],int w[],int outclass[])
{
int i;
int net;
for(i=0;i<5;i++)
{
Net = x[i][0]*w[1]+x[i][1]*w[2]+w[0];
if(net>0)
outclass[i]=1;
else
outclass[i]=-1;
}
}
void main()
{
int p;
int i;
int w[3] = {2, 1, -2}; // theta, w1,w2
// We have five data points in 2-D.
int x[5][2] = {{-1, 2},{0, 2},{3, -2},{0, -3},{-2, -1}};
// Specify the class for each point.
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int inputclass[5] = {1, 1, 1, -1, -1};
// To be determined by the ANN
int outclass[5];
int pclass;
cout<<"The input classes"<<endl;
for(i=0;i<5;i++)
cout<<"point "<<i<<" "<<inputclass[i]<<endl;
evaluate (x, w, outclass);
while (p=misclassified (inputclass,outclass)!=-1)
{
cout<<" inside the while ...";
pclass= inputclass[p];
w[0]=w[0]+ pclass *1;
w[1]=w[1]+ pclass *x[p][0];
w[2]=w[2] + pclass *x[p][1];
evaluate (x,w,outclass);
}
for(i=0;i<5;i++)
cout<<"point"<<i<<" "<<outclass[i]<<endl;
cout<<misclassified (inputclass,outclass);
system("pause");
getch();
}
OUTPUT:
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//Lab # 9. Implement Multi-Layer Perceptron
//Initialize randomly input signals with synaptic weights
//Compute the summation of the neurons and apply MLPs Logistic function.
#include<iostream>
#include<conio.h>
#include<cmath>
using namespace std;
OUTPUT:
void main()
{
double
double
double
double
double
x[2];
w[6];
b1=5,b2=10;
y,h1,h2,L,H;
a = 5 , b = 3;
// genarate input signal x
for(int i=0;i<2;i++)
x[i] = rand() % 9 + 1 ;
// genarate synaptic weight w
for(int i=0;i<6;i++)
w[i] = rand() % 9 + 1 ;
// Calculate h1 and h2 (Hidden layer)
h1 = (x[0]*w[0]) + (x[1]*w[2]) + b1;
h2 = (x[0]*w[1]) + (x[1]*w[1]) + b1;
// Find y ==> outout neuron
y = (h1*w[4]) + (h2*w[5]) + b2;
// Logistic Function
L = 1/(1+exp(-y));
// Display
for(int i=0;i<2;i++)
cout << "Input Sinals "<<i+1 << "=" << x[i] << endl;
for(int i=0;i<6;i++)
cout << "w" << i+1 << "=" << w[i] << endl;
cout<<"Actual Output (y) = "<<y<<endl;
cout << "Logistic value (L ) = "<<L<<endl;
getch();
}
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//Lab # 10. Implement Multi-Layer Perceptron – Forward Signals
//Initialize randomly input signals with synaptic weights
//Compute the summation of the neurons
//Apply MLPs Hyperbolic Tangent Function.
#include<iostream>
#include<conio.h>
#include<cmath>
using namespace std;
void main()
{
double x[2];
double w[6];
double b1=5,b2=10;
double vj,h1,h2,H;
double a = 5 , b = 3;
// genarate input signal x
for(int i=0;i<2;i++)
x[i]=rand()%9+1;
// genarate synaptic weight w
for(int i=0;i<6;i++)
w[i]=rand()%9+1;
// Calculate h1 and h2
h1=(x[0]*w[0]) + (x[1]*w[2])+b1;
h2=(x[0]*w[1]) + (x[1]*w[1])+b1;
// Find y ==> outout neuron
vj=(h1*w[4])+(h2*w[5])+b2;
// Hyperbolic Tangent Function
if(a>0 && b>0)
H=a*tanh(b*vj);
else
H=a*tanh(vj);
// Display
for(int i=0;i<2;i++)
cout<<"x"<<i+1<<"="<<x[i]<<endl;
for(int i=0;i<6;i++)
cout<<"w"<<i+1<<"="<<w[i]<<endl;
cout<<"-------------------------------------------\n";
cout<<"Sum of Neurons (vj) = "<<vj<<endl;
cout<<"H=a * tanh(b*vj(n))"<<endl;
cout<<"H="<<a<<"*tanh("<<b<<"*"<<vj<<")\n";
cout<<"Hyperbolic Tangent Function = "<<H<<endl;
getch();
}
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OUTPUT:
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