Weights in feed-forward backpropogation ANN not changing

I am designing a Feed-Forward BackPropogation ANN with 22 inputs and 1 output (either a 1 or 0). The NN has 3 layers and is using 10 hidden neurons. When I run the NN it only changes the weights a tiny bit and the total error for the output is about 40%. Intially, I thought it was over/under fitting but after I changed the number of hidden neurons, nothing changed.

N is the number of inputs (22)

M is the number of hidden neurons (10)

This is the code that I am using to backpropagate

oin is the output calculated before putting into sigmoid function

oout is the output after going through sigmoid function

double odelta = sigmoidDerivative(oin) * (TARGET_VALUE1[i] - oout);
    double dobias = 0.0;
    double doweight[] = new double[m];

    for(int j = 0; j < m; j++)
    {
        doweight[j] = (ALPHA * odelta * hout[j]) + (MU * (oweight[j] - oweight2[j]));
        oweight2[j] = oweight[j];
        oweight[j] += doweight[j];
    } // j

    dobias = (ALPHA * odelta) + (MU * (obias - obias2));
    obias2 = obias;
    obias += dobias;

    updateHidden(N, m, odelta);

This is the code I am using to change the hidden neurons.

 for(int j = 0; j < m; j++)
        {
            hdelta = (d * oweight[j]) * sigmoidDerivative(hin[j]);

            for(int i = 0; i < n; i++)
            {
                dhweight[i][j] = (ALPHA * hdelta * inputNeuron[i]) + (MU * (hweight[i][j] - hweight2[i][j]));
                hweight2[i][j] = hweight[i][j];
                hweight[i][j] += dhweight[i][j];


            } 

            dhbias[j] = (ALPHA * hdelta) + (MU * (hbias[j] - hbias2[j]));
            hbias2[j] = hbias[j];
            hbias[j] += dhbias[j];
        } `