Neural Network to predict weather not converging
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Hi,
I developed a code to predict weather using neural network without using toolbox. I used the following paper to develop the algorithm:
The problem I am facing in this code is it is not converging:
%-------------------------------------------%
% Reading temperature data
%-------------------------------------------%
filename='STL_2010.csv';
Temp_actual=csvread(filename,1,3,[1, 3, 8759, 3]);
Temp=Temp_actual.*0.001;
%-------------------------------------------%
% Neural Network
%-------------------------------------------%
%Initial temperatures
i=1;
temp24=Temp(i:i+23);
[fut_min_t,min_index]= min(temp24);
[fut_max_t,max_index]= max(temp24);
tempn24=Temp(i:i+23);
min_t_hat_p=fut_min_t;
max_t_hat_p=fut_max_t;
%Neural Network weight initialization
v=rand(28,60);
w1=rand(60,60);
w2=rand(60,60);
w3=rand(60,24);
B=ones(60,24);
k_v=0.0001;
alpha1=0.001;
alpha2=0.001;
alpha3=0.001;
% temp_hat(i:i+23)=rand(1,24);
%Adaptive Scaling
syms m b;
flag=0;
for i=24:24:8736
temp24=Temp(i:i+23);
past_min_t=fut_min_t;
past_max_t=fut_max_t;
fut_min_t= min(temp24);
fut_max_t= max(temp24);
z=[past_min_t;past_max_t;fut_min_t;fut_max_t;tempn24]; %Neural Network Input
%Neural Network
phi1=tanh(v'*z);
phi2=tanh(w1'*phi1);
phi3=tanh(w2'*phi2);
temp_hat(i:i+23)= w3'*phi3; %tanh(w2'*phi2);
error(i:i+23)=temp_hat(i:i+23)-Temp(i:i+23)';
%Adaptive scaling
%Formula: T_scaled=m*T_neural_o/p+bais
min_index_p=min_index;
max_index_p=max_index;
min_t_hat=min(temp_hat(i:i+23));
max_t_hat=max(temp_hat(i:i+23));
[min_t,min_index]=min(Temp(i:i+23));
[max_t,max_index]=max(Temp(i:i+23));
min_index=min_index+i;
max_index=max_index+i;
if min_index_p < max_index_p
[solm, solb] = solve(min_t_hat*m+b==100*fut_min_t, max_t_hat_p*m+b==100*past_max_t);
temp_hat(max_index_p:min_index)= solm*temp_hat(max_index_p:min_index)+solb;
[solm, solb] = solve(min_t_hat*m+b==100*fut_min_t, max_t_hat*m+b==100*fut_max_t);
temp_hat(min_index:max_index)= solm.*temp_hat(min_index:max_index)+solb;
%Error
e(max_index_p:max_index)=temp_hat(max_index_p:max_index)-0.1*Temp_actual(max_index_p:max_index)';
e1(max_index_p:max_index)=0.01.*e(max_index_p:max_index);
else
[solm, solb] = solve(max_t_hat*m+b==100*fut_max_t, min_t_hat_p*m+b==100*past_min_t);
temp_hat(min_index_p:max_index)= solm*temp_hat(min_index_p:max_index)+solb;
[solm, solb] = solve(min_t_hat*m+b==100*fut_min_t, max_t_hat*m+b==100*fut_max_t);
temp_hat(max_index:min_index)= solm*temp_hat(max_index:min_index)+solb;
%Error
e(min_index_p:min_index)=(temp_hat(min_index_p:min_index)-0.1*Temp_actual(min_index_p:min_index)');
e1(min_index_p:min_index)=0.01*e(min_index_p:min_index);
end
%Weight Update
w1=w1-alpha1*phi1*(w1'*phi1+k_v*B*e1(i-23:i)')';
w2=w2-alpha2*phi2*(w2'*phi2+k_v*B*e1(i-23:i)')';
w3=w3+alpha3*phi3*error(i:i+23);
min_t_hat_p=min_t_hat;
max_t_hat_p=max_t_hat;
%Temperature for next day calculation
tempn24=temp24;
flag=flag+1;
end
plot(temp_hat);
hold on;
plot(Temp_actual,'r');
end
If some body can help me diagnose the problem it will be great.
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