Practical-4
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 = x1*w11+x2*w21; zin2
= x1*w21+x2*w22; for i=1:4 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=y1*v1+y2*v2; for
i=1:4
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);
% 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
= x1*w11+x2*w21;
zin2 =
x1*w21+x2*w22; for
i=1:4 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=y1*v1+y2*v2;
for i=1:4 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);
% 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 = x1*w11+x2*w21; zin2
= x1*w21+x2*w22; for i=1:4 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=y1*v1+y2*v2;
for i=1:4 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);
Output:
