put performance to zero

Hello everybody, i created a feedforward network with the following code. I actually try to make prediction with this giving the network an input vector u1 with measured temperature and an interval avout [7,6°C - 22,8°C] and an output vector d1 with measured solar radiation and an interval about [0 W/m^2 - 1196 W/m^2]. These vectors have a lenght of around 10000 lines. If i train the network i start with an initial performance about 1,05e^5 and end around 4,64e^4 after the training. The error os calculated as MSE and lays around 40% from the initial value. Taking an input and output vector with just 30 lines I end with a rest-performace from 2% of the initial error. I actually thought the performace would be better the more data i use, but this is a contradiction to this. Someone got an idea why this training performance is that bad and how i can fix this to get better solutions? I included the plot from the given output (green) vector and the output of the network (red) so u'll see this a little bit better. I hope it helps a bit.

Thx anyway!!!

    j=(1:1:10080);
    r=xlsread('Mai_woche.xlsx');
    u=r(1:10080,1);
    u1=u';
    y=xlsread('Mai_woche.xlsx');
    d=y(1:10080,2);
    d1=d';

    k=10;  
    x=50;
    y=1;
    s=rng;

    iweights=rand(k,y);
    lweights=rand(x,k);
    b1=rand(k,y);
    b2=rand(x,y);

    net=newff(minmax(u1),[k,x,y],{'tansig','tansig','purelin'},'trainlm');
    net.IW{1,1}=iweights;
    net.LW{2,1}=lweights;
    net.b{1}=b1;
    net.b{2}=b2;
    net.IW{1,1}
    net.LW{2,1}

    net.trainParam.show = NaN;
    net.trainParam.lr = 0.04;
    net.trainParam.epochs = 10000;
    net.trainParam.goal = 1;
    net.trainParam.min_grad=1e-30;
    net.trainParam.mu_max=1e18;
    net.trainParam.mu_inc=20;
    net.trainParam.mu_min=1e-5;
    net.performParam.ratio=0.5;
    net.trainParam.time=inf; 

    [net,tr]=train(net,u1,d1);
    a=sim(net,u1);
    e=d1-a;
    perf=mse(e);

    %3. Plot

    plot(j,a,'r',j,d1,'g')

Answer 1

[Christoph]

Nobody knows a solution?...:(