clear;
% Read input file store in one array:

	myfid=fopen('c:\windows\desktop\data5.txt','r');
	A=fscanf(myfid,'%f');
	fclose(myfid);

% initialize inputs:
	
	l0in=[0 0 0 0];

% initialize weights:
	w1=rand(5,4);
	w2=rand(3,5);
	w3=rand(1,3);
	b1=rand(5,1);
	b2=rand(3,1);
	b3=rand(1);
	alpha=0.3;

for mainloop=1:1000
%%%%%%%%%%%% One training set loop %%%%%%%%%%%%%%
for k=1:length(A)-1

% One forward propagation:

%layer0 (delay lines)
	l0in(4)=l0in(3);
	l0in(3)=l0in(2);
	l0in(2)=l0in(1);
	l0in(1)=A(k);

	l0out=l0in';

%layer1 (5 cells)
	net1=w1*l0out + b1;
	l1out=1./(1+exp(-net1))	;	% logsigmoid(net1[x])	

%layer2 (3 cells)
	net2=w2*l1out + b2;
	l2out=1./(1+exp(-net2))	;	% logsigmoid(net2[x])

%layer3 (1 cell- linear)
	net3=w3*l2out + b3;
	l3out=net3;

% One backward propagation:

s3=-2*1*(A(k+1)-l3out);

s2=diag((1-l2out).*l2out)*w3'*s3;

s1=diag((1-l1out).*l1out)*w2'*s2;

% Adjust the weights and biases:

w3=w3-alpha*s3*l2out';	% 1 cell
b3=b3-alpha*s3;
w2=w2-alpha*s2*l1out';	% 3 cells
b2=b2-alpha*s2;
w1=w1-alpha*s1*l0out';	% 5 cells
b1=b1-alpha*s1;

%%%%%%%%%%%% end one training set %%%%%%%%%%%%%%
end

% Find the MSE, after training

	l0in=[0 0 0 0];

for k=1:length(A)-1

%layer0 (delay lines)
	l0in(4)=l0in(3);
	l0in(3)=l0in(2);
	l0in(2)=l0in(1);
	l0in(1)=A(k);

	l0out=l0in';

%layer1 (5 cells)
	net1=w1*l0out + b1;
	l1out=1./(1+exp(-net1))	;	% logsigmoid(net1[x])	

%layer2 (3 cells)
	net2=w2*l1out + b2;
	l2out=1./(1+exp(-net2))	;	% logsigmoid(net2[x])

%layer3 (1 cell - linear)
	net3=w3*l2out + b3;
	l3out=net3;

%Error
	error(k)=A(k+1)-l3out;

end

% Mean square error
	mse(mainloop)=mean(error.*error);	

disp(mainloop);
end