Can I utilize the backpropagation algorithm in a layered, feed-forward ANN in instances where there are multiple output neurons? If so, how? Links to (somewhat) comprehensible resources would be greatly appreciated, if nothing else.
The background is that I'm working on a simple C program that can create, propagate, and train dynamically sized, layered feed-forward ANNs. For the most part, everything's been going swimmingly. However, I'm having a little trouble wrapping my head around backpropogation. My main concern right now is how to use the backpropagation method for training a network that has multiple output neurons. All the examples/explanations I've found only use one output neuron. I'm assuming this is for the sake of simplicity. But, I'm wondering if, perhaps, this is because the backpropagation algorithm is only designed to works with one output neuron at a time. In other words, it can only be applied relative to one output neuron every time an feed-forward ANN is propagated. This would make sense, as it is known to be among the simplest ANN training methods.
Here are links to my aforesaid resources (or at least, the ones I found intuitive):
- https://www.youtube.com/watch?v=IruMm7mPDdM (I'm actually not entirely certain as to whether or not this explanation accounted for multiple output neurons; if it did, it must've went over my head)