%gender voice classification using MLP network
clc;clear all;close all;
%Data Input
p1=double(rand(20,1));
%Target Definition
target=[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1];
W1=double(rand(20,3));
W1_OLD=double(zeros(20,3));
DELTAW1=double(zeros(20,3));
DELTAW1_1=double(zeros(20,1));
DELTAW1_2=double(zeros(20,1));
DELTAW1_3=double(zeros(20,1));
Y=zeros(3,1);
YF=zeros(3,1);
dY=zeros(3,1);
W2=double(rand(3,1));
W2_OLD=double(zeros(3,1));
DELTAW2=double(zeros(3,1));
V=zeros(1,1);
O=zeros(1,50);
E=0;
etha=.4;
flag=0;
epoch=0;
while flag==0
epoch=epoch+1;
for n=1:50;
O=zeros(1,50);
Y=W1'*p1;
YF=logsig(Y);
V=W2'*YF;
O(1,n)=logsig(V); %Actual output
dV=exp(-V)/(1+exp(-V))^2;
Y1=Y(1,1);
Y2=Y(2,1);
Y3=Y(3,1);
dY1=exp(-Y1)/(1+exp(-Y1))^2;
dY2=exp(-Y2)/(1+exp(-Y2))^2;
dY3=exp(-Y3)/(1+exp(-Y3))^2;
dY=[dY1;dY2;dY3];
if O(1,n)~=target(1,n)
% weight update
E=target(1,n)-O(1,n);
DELTAW2=etha*E*Y*dV;
W2=W2+DELTAW2;
DELTAW1_1=etha*E*dV*p1*W2(1,1)*dY1;
DELTAW1_2=etha*E*dV*p1*W2(2,1)*dY2;
DELTAW1_3=etha*E*dV*p1*W2(3,1)*dY3;
DELTAW1=[DELTAW1_1,DELTAW1_2,DELTAW1_3];
W1=W1+DELTAW1;
%else
%flag=1;
end
%WW=W1-W1_OLD;
if all(W2==W2_OLD) && all(W1==W1_OLD) || epoch==100
flag=1;
else
%if all(W1~=W1_OLD)
W1_OLD=W1;
%else
W2_OLD=W2;
%end
end
end
end
%-------------------------------------------------------------------
%-------------------------------------------------------------------
%Test Algorithm
test=p1;
for n=1:50;
O=zeros(1,50);
Y=W1'*test;
YF=logsig(Y);
V=W2'*YF;
O(1,n)=logsig(V); %Actual output
%imshow(O);
O
end
