Neural Net Backprop Weight updating Pseudo code help please
It looks like you're adding the delta to the weights instead of subtracting it. Gradient descent is given by the following calculation run for some iterations:

$$
x^{current} = x^{previous} - \alpha \frac{dy}{dx}
$$

where $\alpha$ is the learning rate. We subtract the value because derivatives go in the direction of steepest ascent. So the following lines:

HIDDEN_OUTPUT_WEIGHTS[ i ] += hi_weight_delta;
INPUT_HIDDEN_WEIGHTS[ i ][ j ] += ij_weight_delta;


should be

HIDDEN_OUTPUT_WEIGHTS[ i ] -= hi_weight_delta;
INPUT_HIDDEN_WEIGHTS[ i ][ j ] -= ij_weight_delta;


Additionally, I'm not sure what the architecture of your network is, but it doesn't look like you have bias units- consider adding them so that your neurons can have non-zero centered activations. Not having them may impact your network's ability to learn.