I am created one small neural network and I am trying to program that network.I am getting different results when I am trying to simulate it.
Please find the attached file and help me.
%%%%%%%%%%%%%%%%%%% please find the code%%%%%%%%%%%%
Input1 = [1 100 200 300];
Input2 = [400 500 600 700];
Input3 = [800 900 1000 2000];
Input4 = [2 102 203 3];
Output1 = [0 10 20 30 ];
Output2 = [40 50 60 70 ];
Output3 = [80 90 100 200 ];
I=[Input1 Input2 Input3]; O=[Output1 Output2 Output3];
%%%%%%%%%% function for training the inputs and oyutputs; net = feedforwardnet([10 5]);%%%%%%%%%%%%%feed forward network%%%%%%%%
% train net net.trainParam.epochs = 1000; % net.trainParam.show = 10; % net.trainParam.goal = 0.1; net.divideParam.trainRatio = 0.7; % training set [%] net.divideParam.valRatio = 0.15; % validation set [%] net.divideParam.testRatio = 0.15; % test set [%] % net.efficiency.memoryReduction = 1000; [net,tr] = train(net,I,O);% train a neural network view(net); wb = formwb(net,net.b,net.iw,net.lw); [b,iw,lw] = separatewb(net,wb);
y=net(Input1)
%%%%%%%%%%%%%%%%%%%%%%validating network using code%%%%%%%%%%%%%%%%%%%%%%%
b1=[2.57310486009111 1.93785769426316 -1.46320033175527 -0.842004107257358 -0.286323193468700 0.286827247650549 %%%%%%%%%%%%%%%%%%%%%%%%%%%weights obtained after training%%%%%%%%%%%%%%%%%%%%%%%%%%%% 0.831493499871035 -1.39671607955901 -1.90380904621022 -2.45493214632677];
b2=[1.83574067690576 0.774732933645161 -0.0847258567572143 0.938593059039810 1.46771777356798];
b3=[0.420197391190367 0.115212450273526 -0.759763338095833 -0.250672959073144];
%%%%%%%%%%%%%%%%%%%%%%%%%%%weights obtained after training%%%%%%%%%%%%%%%%%%%%%%%%%%%%
iW=[-1.38166200670922 0.957490912286695 0.515857183896969 -1.67802011516874 -0.607219159206796 1.21648695700533 -1.52373804094426 -1.42190529155021 0.959885562980659 -1.82824861935095 0.812406556958687 1.04963708328287 0.106290246812093 -1.29433065994092 1.70115025195897 1.23078410448700 1.31076122407300 -0.517144473527336 -2.08491227030795 0.284098191355632 0.830270776449073 1.58153232049612 -0.834366455134675 1.51390644460052 1.91950733164198 0.0871757775656829 -1.56946254953226 0.0784020062364014 -2.03145306741346 1.06527607936168 0.886361361008305 0.324013786621172 -1.25556535930711 -1.27864849126282 -1.24741299098989 -1.21464494025010 -0.368339680827727 0.851405223284353 2.34576432829584 0.0434109884245971];
%%%%%%%%%%%%%%%%%%%%%%%%%%%weights obtained after training%%%%%%%%%%%%%%%%%%%%%%%%%%%%
lW1=[-0.602609989946975 0.525733750947808 -0.203602085475660 0.222921297850082 -0.179900498642999 -0.482399972861968 0.727409811592458 0.692255327028914 0.343955316924856 -0.686890820583700 -0.795542517778123 0.307953699635952 -0.270964970640794 -0.435776642224120 0.924207180182342 -0.475816746204693 -0.170114502128735 0.833297282454862 -0.0518751068652391 -0.494048413548348 -0.378651557509865 0.381275940064419 0.879407533770447 0.654548914748321 0.841996207871969 -0.486494949311301 0.0926006882680324 0.389713200473236 0.438691192343393 0.250420511382062 0.401305604144410 0.807228415644053 -0.675581969078475 0.0269187271775576 0.156864083817956 -0.677534259590177 -0.355312921627937 0.449415578346035 0.546280498052905 0.429541160955270 0.386338268169618 0.0556388076959695 0.190289127159856 0.761310740378647 -0.610789120692214 -0.541220414173197 0.681280059126398 -0.0679901158816549 -0.779139708993143 0.654725685947907];
%%%%%%%%%%%%%%%%%%%%%%%%%%%weights obtained after training%%%%%%%%%%%%%%%%%%%%%%%%%%%%
lW2=[-0.997829228826340 -0.738244586915366 0.138812837427359 0.363607240326660 0.693990178780186 -0.644765560994143 -1.04877878583043 0.0449859320288510 0.543366970025662 0.521704189739841 -0.107487306272888 0.123220178980386 -0.850671952687010 -0.765452096422727 0.686182723836282 -0.129335421277810 -0.895531315080272 -0.574093351224515 -0.157806595675016 -0.211448632638790];
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%Input layer%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% r1=b1(1,1)+(Input1(1,1)*iW(1,1))+(Input1(2,1)*iW(1,2))+(Input1(3,1)*iW(1,3))+(Input1(4,1)*iW(1,4)); hidden1= (1/(1+exp(-r1))); r2=b1(2,1)+(Input1(1,1)*iW(2,1))+(Input1(2,1)*iW(2,2))+(Input1(3,1)*iW(2,3))+(Input1(4,1)*iW(2,4)); hidden2=(1/(1+exp(-r2))); r3=b1(3,1)+(Input1(1,1)*iW(3,1))+(Input1(2,1)*iW(3,2))+(Input1(3,1)*iW(3,3))+(Input1(4,1)*iW(3,4)); hidden3=(1/(1+exp(-r3))); r4=b1(4,1)+(Input1(1,1)*iW(4,1))+(Input1(2,1)*iW(4,2))+(Input1(3,1)*iW(4,3))+(Input1(4,1)*iW(4,4)); hidden4=(1/(1+exp(-r4))); r5=b1(5,1)+(Input1(1,1)*iW(5,1))+(Input1(2,1)*iW(5,2))+(Input1(3,1)*iW(5,3))+(Input1(4,1)*iW(5,4)); hidden5=(1/(1+exp(-r5))); r6=b1(6,1)+(Input1(1,1)*iW(6,1))+(Input1(2,1)*iW(6,2))+(Input1(3,1)*iW(6,3))+(Input1(4,1)*iW(6,4)); hidden6=(1/(1+exp(-r6))); r7=b1(7,1)+(Input1(1,1)*iW(7,1))+(Input1(2,1)*iW(7,2))+(Input1(3,1)*iW(7,3))+(Input1(4,1)*iW(7,4)); hidden7=(1/(1+exp(-r7))); r8=b1(8,1)+(Input1(1,1)*iW(8,1))+(Input1(2,1)*iW(8,2))+(Input1(3,1)*iW(8,3))+(Input1(4,1)*iW(8,4)); hidden8=(1/(1+exp(-r8))); r9=b1(9,1)+(Input1(1,1)*iW(9,1))+(Input1(2,1)*iW(9,2))+(Input1(3,1)*iW(9,3))+(Input1(4,1)*iW(9,4)); hidden9=(1/(1+exp(-r9))); r10=b1(10,1)+(Input1(1,1)*iW(10,1))+(Input1(2,1)*iW(10,2))+(Input1(3,1)*iW(10,3))+(Input1(4,1)*iW(10,4)); hidden10=(1/(1+exp(-r10)));
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%Hidden layer1%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% r11=[b2(1,1), (hidden1*lW1(1,1)), (hidden2*lW1(1,2)), (hidden3*lW1(1,3)), (hidden4*lW1(1,4)), (hidden5*lW1(1,5)), (hidden6*lW1(1,6)), (hidden7*lW1(1,7)) ,(hidden8*lW1(1,8)) ,(hidden9*lW1(1,9)) ,(hidden10*lW1(1,10))]; r11n=nansum(r11); hidden11=(1/(1+exp(-r11n))); r12=[b2(2,1),(hidden1*lW1(2,1)),(hidden2*lW1(2,2)),(hidden3*lW1(2,3)),(hidden4*lW1(2,4)),(hidden5*lW1(2,5)),(hidden6*lW1(2,6)),(hidden7*lW1(2,7)),(hidden8*lW1(2,8)),(hidden9*lW1(2,9)),(hidden10*lW1(2,10))]; r12n=nansum(r12); hidden12=(1/(1+exp(-r12n))); r13=[b2(3,1),(hidden1*lW1(3,1)),(hidden2*lW1(3,2)),(hidden3*lW1(3,3)),(hidden4*lW1(3,4)),(hidden5*lW1(3,5)),(hidden6*lW1(3,6)),(hidden7*lW1(3,7)),(hidden8*lW1(3,8)),(hidden9*lW1(3,9)),(hidden10*lW1(3,10))]; r13n=nansum(r13); hidden13=(1/(1+exp(-r13n))); r14=[b2(4,1),(hidden1*lW1(4,1)),(hidden2*lW1(4,2)),(hidden3*lW1(4,3)),(hidden4*lW1(4,4)),(hidden5*lW1(4,5)),(hidden6*lW1(4,6)),(hidden7*lW1(4,7)),(hidden8*lW1(4,8)),(hidden9*lW1(4,9)),(hidden10*lW1(4,10))]; r14n=nansum(r14); hidden14=(1/(1+exp(-r14n))); r15=[b2(5,1),(hidden1*lW1(5,1)),(hidden2*lW1(5,2)),(hidden3*lW1(5,3)),(hidden4*lW1(5,4)),(hidden5*lW1(5,5)),(hidden6*lW1(5,6)),(hidden7*lW1(5,7)),(hidden8*lW1(5,8)),(hidden9*lW1(5,9)),(hidden10*lW1(5,10))]; r15n=nansum(r15); hidden15=(1/(1+exp(-r15n)));
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%Hidden layer2%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% r16=[b3(1,1),(hidden11*lW2(1,1)),(hidden12*lW2(1,2)),(hidden13*lW2(1,3)),(hidden14*lW2(1,4)),(hidden15*lW2(1,5))]; r16n=nansum(r16); r17=[b3(2,1),(hidden11*lW2(2,1)),(hidden12*lW2(2,2)),(hidden13*lW2(2,3)),(hidden14*lW2(2,4)),(hidden15*lW2(2,5))]; r17n=nansum(r17); r18=[b3(3,1),(hidden11*lW2(3,1)),(hidden12*lW2(3,2)),(hidden13*lW2(3,3)),(hidden14*lW2(3,4)),(hidden15*lW2(3,5))]; r18n=nansum(r18); r19=[b3(4,1),(hidden11*lW2(4,1)),(hidden12*lW2(4,2)),(hidden13*lW2(4,3)),(hidden14*lW2(4,4)),(hidden15*lW2(4,5))]; r19n=nansum(r19);
disp(r16n); disp(r17n); disp(r18n); disp(r19n);
please suggest the changes so that i get same output using neural network and program.
