TPCT’s
College of Engineering, Osmanabad
Department of Computer Science and Engineering
Laboratory Manual
Soft Computing
For
Final Year Students
Manual Prepared by
Mr.R.A.Sarwade
Author COE, Osmanabad
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Soft Computing Laboratory Manual for Final Year Students, Schemes and Mind Maps of Software Engineering
A laboratory manual for final year computer science and engineering students studying soft computing at tpct's college of engineering, osmanabad. The manual includes matlab programs for implementing andnot, xor, and and functions using mcculloch-pitts neural net and perceptron. It also covers pattern storage using discrete hopfield network.
Typology: Schemes and Mind Maps
2023/2024
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TPCT’s
College of Engineering, Osmanabad
Department of Computer Science and Engineering
Laboratory Manual
Soft Computing
For
Final Year Students
Manual Prepared by
Mr.R.A.Sarwade
Author COE, Osmanabad
TPCT’s
College of Engineering
Solapur Road, Osmanabad
Department of Computer Science and Engineering
1) Vision of the Department:
To achieve and evolve as a center of academic excellence and research center in the field of Computer Science and Engineering. To develop computer engineers with necessary analytical ability and human values who can creatively design, implement a wide spectrum of computer systems for welfare of the society.
2) Mission of the Department:
The department strives to continuously engage in providing the students with in-depth understanding of fundamentals and practical training related to professional skills and their applications through effective Teaching- Learning Process and state of the art laboratories pertaining to CSE and inter disciplinary areas. Preparing students in developing research, design, entrepreneurial skills and employability capabilities.
FOREWORD
It is my great pleasure to present this laboratory manual for Final year Engineering students for the subject – “ Soft Computing”.
As a student, many of you may be wondering with some of the questions in your mind regarding the subject and exactly what has been tried is to answer through this manual
Faculty members are also advised that covering these aspects in initial stage itself will greatly relieve them in future as much of the load will be taken care by the enthusiasm energies of the students once they are conceptually clear
H.O.D.
CSE Dept.
LABORATORY MANUAL CONTENTS
This manual is intended for the final year students of Computer Science and Engineering in the subject of Soft Computing. This manual typically contains practical/Lab Sessions related to the subject on the MATLAB/Scilab platform covering various aspects of the subject to enhance understanding.
Students' are advised to thoroughly go through this manual rather than only topics mentioned in the syllabus as practical aspects are the key to understanding and conceptual visualization of theoretical aspects covered in the books.
Mr. R.A.Sarwade.
1.Do’s and Don’ts in the laboratory
1. Make entry in the Log Book as soon as you enter the Laboratory.
2. All the students should sit according to their roll numbers starting from their left
to right.
3. All the students are supposed to enter the terminal number in the log book.
4. Do not change the terminal on which you are working.
5. All the students are expected to get at least the algorithm of the program/concept
to be implemented.
6. Strictly observe the instructions given by the teacher/Lab Instructor
Instruction for Laboratory Teachers::
1. Submission related to whatever lab work has been completed should be done
during the next lab session. The immediate arrangements for printouts related to
submission on the day of practical assignments.
2. Students should be taught for taking the printouts under the observation of lab
teacher
Pre-lab (Introduction to MATLAB)
Questions:
1] What do you mean by MATLAB? 2] Describe all MATLAB windows in detail.(command window, Graphics window , Editor window, Workspace, command history, current directory) 3] Describe all basic MATLAB command with example
else
disp('Net is not learning Enter another set of weights and threshold value');
w1=input('Weight w1=');
w2=input('Weight w2=');
theta=input('theta=');
end
end
disp('McCulloch Pitts Net for ANDNOT function');
disp('Weights of neuron');
disp(w1);
disp(w2);
disp('Threshold value=');
disp(theta);
Output :-
Enter the weights
Weight w1=
Weight w2=
Enter threshold value
theta=
Output of net= 1 0 0 0
McCulloch Pitts Net for AND function
Weights of neuron
Threshold value=
EXPERIMENT NO. 2
Title: Generate ANDNOT function using McCulloch-Pitts neural net by MATLAB Program.
Objectives:
- To implement ANDNOT logic gate
Instructions: 1.Study MP Model 2.Study logic gates: AND,OR,NOT,Exclusive-OR 3.Write a program to implement ANDNOT gate without using neural network toolbox.
Program to implement ANDNOT gate:
%ANDNOT function using McCulloch-Pitts neuron clear; clc; % Getting weights and threshold value disp('Enter the weights'); w1=input('Weight w1='); w2=input('Weight w2='); disp('Enter threshold value'); theta=input('theta='); y=[0 0 0 0]; x1=[0 0 1 1]; x2=[0 1 0 1]; z=[0 0 1 0]; con=1; while con zin = x1w1+x2w2; for i=1: if zin(i)>=theta y(i)=1; else y(i)=0; end end disp('Output of net='); disp(y); if y==z con=0; else
EXPERIMENT NO. 3
Title: Generate XOR function using McCulloch-Pitts neural net by MATLAB program.
Objectives:
- To implement XOR logic gate
Instructions: 1.Study MP Model 2.Study logic gates: AND,OR,NOT,Exclusive-OR 3.Write a program to implement XOR gate without using neural network toolbox.
Program to implement XOR gate:
% XOR function using McCulloch-Pitts neuron clear; clc; % Getting weights and threshold value disp('Enter the weights'); w11=input('Weight w11='); w12=input('Weight w12='); w21=input('Weight w21='); w22=input('Weight w22='); v1=input('Weight v1='); v2=input('Weight v2='); disp('Enter threshold value'); theta=input('theta='); x1=[0 0 1 1]; x2=[0 1 0 1]; z=[0 1 1 0]; con=1; while con zin1 = x1w11+x2w21; zin2 = x1w21+x2w22; for i=1: if zin1(i)>=theta y1(i)=1; else y1(i)=0; end if zin2(i)>=theta y2(i)=1; else y2(i)=0; end end
yin=y1v1+y2v2; for i=1: if yin(i)>=theta; y(i)=1; else y(i)=0; end end disp('Output of net='); disp(y); if y==z con=0; else disp('Net is not learning Enter another set of weights and threshold value'); w11=input('Weight w11='); w12=input('Weight w12='); w21=input('Weight w21='); w22=input('Weight w22='); v1=input('Weight v1='); v2=input('Weight v2='); theta=input('theta='); end end disp('McCulloch Pitts Net for XOR function'); disp('Weights of neuron Z1'); disp(w11); disp(w21); disp('Weights of neuron Z2'); disp(w12); disp(w22); disp('Weights of neuron Y'); disp(v1); disp(v2); disp('Threshold value='); disp(theta);
EXPERIMENT NO. 4
Title: Write a program for solving linearly separable problem using Perceptron Model.
Objectives: To implement AND function using Perceptron
Instructions:
- Study Perceptron Model
- Write a program to implement AND function
- Draw the diagram of AND using Perceptron used below
Program to implement AND function:
%Perceptron for AND funtion
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 con
con=0;
for i=1:
yin=b+x(1,i)w(1)+x(2,i)w(2);
if yin>theta
y=1;
end
if yin <=theta & yin>=-theta
y=0;
end
if yin<-theta
y=-1;
end
if y-t(i)
con=1;
for j=1:
w(j)=w(j)+alphat(i)x(j,i);
end
b=b+alpha*t(i);
end
end
epoch=epoch+1;
end
disp('Perceptron for AND function');
disp(' Final Weight matrix');
disp(w);
disp('Final Bias');
disp(b);
Output
Enter Learning rate=
Enter Threshold value=0.
Perceptron for AND function
Final Weight matrix
Final Bias
end epoch=epoch+1; end
disp('Perceptron for AND Function'); disp('Final Weight Matrix'); disp(w); disp('Final Bias'); disp(b);
Output :-
Enter Learning rate= Enter Threshold Value=0. Perceptron for AND Function Final Weight Matrix 1 1 Final Bias
EXPERIMENT NO. 6
Title: Write a program to store a pattern (1 1 1 0). Test the network using Discrete Hopfield Net by giving the input with mistakes in First and Second position.
Objectives: To understand the concept of Pattern storage To implement Discrete Hopfield Net
Training Algorithm: Describe about Pattern Storage networks Write theory for Hopfield Net.
MATLAB Source Code:
%Discrete hopfield net clc; clear; x=[1 1 1 0]; tx=[0 0 1 0]; w=(2x'-1)(2x-1); for i=1: w(i,i)=0; end con=1; y=[0 0 1 0]; while con up=[4 2 1 3]; for i=1: yin(up(i))=tx(up(i))+yw(1:4,up(i)); if yin(up(i))> y(up(i))=1; end end if y==x disp('convergence has been obtained'); disp('the converged output'); disp(y); con=0; end end
Output:
%convergence has been obtained %the converged output % 1 1 1