bright_hill asked . 2021-05-14

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input= xlsread('Input');



output = xlsread('Output');

bias = ones(1,3)*0.0005;      %bias values 

a=-1;
b=1;

rng('default')
V= a+(b-a)*rand(6,5);          %weights between the input(6 nodes) and hidden(5 nodes) layers
rng('default')
W = a+(b-a)*rand(5,1);         %weights between the hidden(5 nodes) and output(1 node) layers

transposed_input = input';     %inputs
d = output';                   %target output

                %initialization of del[W] and del[V]

del_W1=0;
del_W2=0;
del_W3=0;
del_W4=0;
del_W5=0;


del_V1=0;
del_V2=0;
del_V3=0;
del_V4=0;
del_V5=0;
del_V6=0;

del_V7=0;
del_V8=0;
del_V9=0;
del_V10=0;
del_V11=0;
del_V12=0;

del_V13=0;
del_V14=0;
del_V15=0;
del_V16=0;
del_V17=0;
del_V18=0;

del_V19=0;
del_V20=0;
del_V21=0;
del_V22=0;
del_V23=0;
del_V24=0;

del_V25=0;
del_V26=0;
del_V27=0;
del_V28=0;
del_V29=0;
del_V30=0;

for epoch=1:6000      %number of iterations

alpha=0.1;            %learning rate

Mu=0.1;               %Momentum Constant 


N=80;                 %Number of training cases





for k=1:N
    
    %Feed-Forward Network starts
    
    %Output to input neurons                                                          
    
    OI(1,k)=1./(1+exp(-(transposed_input(1,k)+bias(1,1))));
    OI(2,k)=1./(1+exp(-(transposed_input(2,k)+bias(1,1))));
    OI(3,k)=1./(1+exp(-(transposed_input(3,k)+bias(1,1))));
    OI(4,k)=1./(1+exp(-(transposed_input(4,k)+bias(1,1))));
    OI(5,k)=1./(1+exp(-(transposed_input(5,k)+bias(1,1))));
    OI(6,k)=1./(1+exp(-(transposed_input(6,k)+bias(1,1))));
    
     %Input to hidden neurons   
    
    IH1(1,k)= (OI(1,k)*V(1,1))+(OI(2,k)*V(2,1))+ (OI(3,k)*V(3,1))+ (OI(4,k)*V(4,1))+ (OI(5,k)*V(5,1))+(OI(6,k)*V(6,1))+bias(1,2);
    IH2(1,k)= (OI(1,k)*V(1,2))+(OI(2,k)*V(2,2))+ (OI(3,k)*V(3,2))+ (OI(4,k)*V(4,2))+ (OI(5,k)*V(5,2))+(OI(6,k)*V(6,2))+bias(1,2);
    IH3(1,k)= (OI(1,k)*V(1,3))+(OI(2,k)*V(2,3))+ (OI(3,k)*V(3,3))+ (OI(4,k)*V(4,3))+ (OI(5,k)*V(5,3))+(OI(6,k)*V(6,3))+bias(1,2);
    IH4(1,k)= (OI(1,k)*V(1,4))+(OI(2,k)*V(2,4))+ (OI(3,k)*V(3,4))+ (OI(4,k)*V(4,4))+ (OI(5,k)*V(5,4))+(OI(6,k)*V(6,4))+bias(1,2);
    IH5(1,k)= (OI(1,k)*V(1,5))+(OI(2,k)*V(2,5))+ (OI(3,k)*V(3,5))+ (OI(4,k)*V(4,5))+ (OI(5,k)*V(5,5))+(OI(6,k)*V(6,5))+bias(1,2);
    
    %Output to hidden neurons 

    OH1(1,k) = (exp(IH1(1,k)+bias(1,2)) - exp(-(IH1(1,k)+bias(1,2))))./(exp(IH1(1,k)+bias(1,2)) + exp(-(IH1(1,k)+bias(1,2))));
    OH2(1,k) = (exp(IH2(1,k)+bias(1,2)) - exp(-(IH2(1,k)+bias(1,2))))./(exp(IH2(1,k)+bias(1,2)) + exp(-(IH2(1,k)+bias(1,2))));
    OH3(1,k) = (exp(IH3(1,k)+bias(1,2)) - exp(-(IH3(1,k)+bias(1,2))))./(exp(IH3(1,k)+bias(1,2)) + exp(-(IH3(1,k)+bias(1,2))));
    OH4(1,k) = (exp(IH4(1,k)+bias(1,2)) - exp(-(IH4(1,k)+bias(1,2))))./(exp(IH4(1,k)+bias(1,2)) + exp(-(IH4(1,k)+bias(1,2))));
    OH5(1,k) = (exp(IH5(1,k)+bias(1,2)) - exp(-(IH5(1,k)+bias(1,2))))./(exp(IH5(1,k)+bias(1,2)) + exp(-(IH5(1,k)+bias(1,2))));

    %Input to Output neuron 
    
    IO(1,k)=OH1(1,k)*W(1,1)+OH2(1,k)*W(2,1)+OH3(1,k)*W(3,1)+OH4(1,k)*W(4,1)+OH5(1,k)*W(5,1)+bias(1,3);

    %Output to Output neuron

    Out(1,k)=IO(1,k)+bias(1,3);

   %forward step calculation corresponding to each training case of a batch run terminates here
    

   %BackPropagation of [V] and [W] starts
    %Finding Mean Squared Error(E)

    error(1,k)=d(1,k)-Out(1,k);
    diff_Out(1,k)=[error(1,k).^2];
    E= sum(diff_Out(1,k))./(2*N); 

  end

    while (E >= 0.0004)          %convergence criteria
	
    for p=1:N 
   
    delta(1,p)=Out(1,p)*(1-Out(1,p))*error(1,p);
    del=sum(delta(1,p));

                                 %summation of Output to hidden neurons for 'k' training cases
    OHS1=sum(OH1(1,p));
    OHS2=sum(OH2(1,p));  
    OHS3=sum(OH3(1,p));  
    OHS4=sum(OH4(1,p));  
    OHS5=sum(OH5(1,p)); 
         
    
                                          %summation of Output to input neurons for 'k' training cases
    
    OI1=sum(OI(1,p));
    OI2=sum(OI(2,p));
    OI3=sum(OI(3,p));
    OI4=sum(OI(4,p));
    OI5=sum(OI(5,p));
    OI6=sum(OI(6,p));
    
     end      


    delta2_1=OHS1*(1-OHS1)*W(1,1)*del;
    delta2_2=OHS2*(1-OHS2)*W(2,1)*del;
    delta2_3=OHS3*(1-OHS3)*W(3,1)*del;         
    delta2_4=OHS4*(1-OHS4)*W(4,1)*del;
    delta2_5=OHS5*(1-OHS5)*W(5,1)*del;
    
    

                                                     %delta[W] 

    del_W1=-alpha*OHS1*del+Mu*del_W1;
    
    del_W2=-alpha*OHS2*del+Mu*del_W2;
    
       
    del_W3=-alpha*OHS3*del+Mu*del_W3;
    
        
    del_W4=-alpha*OHS4*del+Mu*del_W4;
   
        
    del_W5=-alpha*OHS5*del+Mu*del_W5;
                                              
                                              %delta[V]

    del_V1=-alpha*OI1*delta2_1+Mu*del_V1;
    del_V2=-alpha*OI2*delta2_1+Mu*del_V2;
    del_V3=-alpha*OI3*delta2_1+Mu*del_V3;
    del_V4=-alpha*OI4*delta2_1+Mu*del_V4;
    del_V5=-alpha*OI5*delta2_1+Mu*del_V5;
    del_V6=-alpha*OI6*delta2_1+Mu*del_V6;

    
    
    del_V7=-alpha*OI1*delta2_2+Mu*del_V7;
    del_V8=-alpha*OI2*delta2_2+Mu*del_V8;
    del_V9=-alpha*OI3*delta2_2+Mu*del_V9;
    del_V10=-alpha*OI4*delta2_2+Mu*del_V10;
    del_V11=-alpha*OI5*delta2_2+Mu*del_V11;
    del_V12=-alpha*OI6*delta2_2+Mu*del_V12;
    
    

    del_V13=-alpha*OI1*delta2_3+Mu*del_V13;
    del_V14=-alpha*OI2*delta2_3+Mu*del_V14;
    del_V15=-alpha*OI3*delta2_3+Mu*del_V15;
    del_V16=-alpha*OI4*delta2_3+Mu*del_V16;
    del_V17=-alpha*OI5*delta2_3+Mu*del_V17;
    del_V18=-alpha*OI6*delta2_3+Mu*del_V18;
    
    

    del_V19=-alpha*OI1*delta2_4+Mu*del_V19;
    del_V20=-alpha*OI2*delta2_4+Mu*del_V20;
    del_V21=-alpha*OI3*delta2_4+Mu*del_V21;
    del_V22=-alpha*OI4*delta2_4+Mu*del_V22;
    del_V23=-alpha*OI5*delta2_4+Mu*del_V23;
    del_V24=-alpha*OI6*delta2_4+Mu*del_V24;
    
   

    del_V25=-alpha*OI1*delta2_5+Mu*del_V25;
    del_V26=-alpha*OI2*delta2_5+Mu*del_V26;
    del_V27=-alpha*OI3*delta2_5+Mu*del_V27;
    del_V28=-alpha*OI4*delta2_5+Mu*del_V28;
    del_V29=-alpha*OI5*delta2_5+Mu*del_V29;
    del_V30=-alpha*OI6*delta2_5+Mu*del_V30;
 
      
     
             end
        
 

    
   %updated [W] and [V] matrix

    W(1,1)=W(1,1)+del_W1;
    W(2,1)=W(2,1)+del_W2;
    W(3,1)=W(3,1)+del_W3;
    W(4,1)=W(4,1)+del_W4;
    W(5,1)=W(5,1)+del_W5;

    V(1,1)= V(1,1)+del_V1;
    V(2,1)= V(2,1)+del_V2;
    V(3,1)= V(3,1)+del_V3;
    V(4,1)= V(4,1)+del_V4;
    V(5,1)= V(5,1)+del_V5;
    V(6,1)= V(6,1)+del_V6;

    V(1,2)= V(1,2)+del_V7;
    V(2,2)= V(2,2)+del_V8;
    V(3,2)= V(3,2)+del_V9;
    V(4,2)= V(4,2)+del_V10;
    V(5,2)= V(5,2)+del_V11;
    V(6,2)= V(6,2)+del_V12;

    V(1,3)= V(1,3)+del_V13;
    V(2,3)= V(2,3)+del_V14;
    V(3,3)= V(3,3)+del_V15;
    V(4,3)= V(4,3)+del_V16;
    V(5,3)= V(5,3)+del_V17;
    V(6,3)= V(6,3)+del_V18;


    V(1,4)= V(1,4)+del_V19;
    V(2,4)= V(2,4)+del_V20;
    V(3,4)= V(3,4)+del_V21;
    V(4,4)= V(4,4)+del_V22;
    V(5,4)= V(5,4)+del_V23;
    V(6,4)= V(6,4)+del_V24;



    V(1,5)= V(1,5)+del_V25;
    V(2,5)= V(2,5)+del_V26;
    V(3,5)= V(3,5)+del_V27;
    V(4,5)= V(4,5)+del_V28;
    V(5,5)= V(5,5)+del_V29;
    V(6,5)= V(6,5)+del_V30;
end

deep learning , matlab , simulink