I have a quick question regarding backpropagation. I am looking at the following:

__http://www4.rgu.ac.uk/files/chapter3%20-%20bp.pdf__

In this paper, it says to calculate the error of the neuron as:

Error = Output(i) * (1 - Output(i)) * (Target(i) - Output(i))

I have put the part of the equation that I don't understand in bold. In the paper, it says that the Output(i) * (1 - Output(i)) term is needed because of the sigmoid function - but I still don't understand why this would be necessary.

What would be wrong with using

Error = abs(Output(i) - Target(i))

?

Is the error function regardless of the neuron activation/transfer function?