Simple Linear Neural Network Weights from Training , are not compatable with training results, cant understand why… Matlab 2011a

I have a very strange problem The weights that I get from training, when implied directly on input , return diffrent results!!! Ill show it on a very simple examaple lets say we have an input vector x= 0:0.01:1; and target vector t=x^2 (I know it better to use non linear network ) after training ,2 layer ,linear network ,with one neuron at each layer, we get:

sim(net,0.95) = 0.7850 (some error in training - thats ok and should be ) weights from net.IW,net.LW,net.b:

IW =

0.4547 LW =

2.1993 b =

0.3328 -1.0620 if I use the weights : Out = purelin(purelin(0.95*IW+b(1))*LW+b(2)) = 0.6200! , I get different result from the result of the sim!!! how can it be? whats wrong? after several hours of exploring , Im desperate:-(

the code:

%Main_TestWeights close all clear all clc

t1 = 0:0.01:1; x = t1.^2;

hiddenSizes = 1; net = feedforwardnet(hiddenSizes);

[Xs,Xi,Ai,Ts,EWs,shift] = preparets(net,con2seq(t1),con2seq(x)); net.layers{1,1}.transferFcn = 'purelin'; [net,tr,Y,E,Pf,Af] = train(net,Xs,Ts,Xi,Ai); view(net);

IW = cat(2,net.IW{1}); LW = cat(2,net.LW{2,1}); b = cat(2,[net.b{1,1},net.b{2,1}]);

%REsult from Sim t2=0.95; Yk = sim(net,t2)

%Result from Weights x1 = IW*t2'+b(1) x1out = purelin(x1) x2 = purelin(x1out*(LW)+b(2))

Answer 1

[R]

thank you very much that realy helped I used mapminmax to get the real values of data(on inout and otpput)

[Greg Heath]

Accepting your response to my answer?

ARRGGGGHHHH!

Answer 2

[Greg Heath]

Feedforwardnet automatically uses mapminmax to transform inputs and targets to [-1,1].

help feedforwardnet

doc feedforwardnet

Therefore, the weights should be applied to the normalized input and the normalized output should be unnormalized.

Hope this helps.

Greg