Derivation of the Backpropagation Algorithm Based on Derivative Amplification Coefficients
This paper aims to provide a more understandable derivation of the backpropagation algorithm for students and researchers struggling with existing explanations, offering an incremental improvement in pedagogical clarity.
This paper presents a new derivation of the backpropagation algorithm using derivative amplification coefficients. It extends a concept previously applied to fully connected cascade networks to conventional feedforward neural networks, enabling a rigorous and simpler derivation.
The backpropagation algorithm for neural networks is widely felt hard to understand, despite the existence of some well-written explanations and/or derivations. This paper provides a new derivation of this algorithm based on the concept of derivative amplification coefficients. First proposed by this author for fully connected cascade networks, this concept is found to well carry over to conventional feedforward neural networks and it paves the way for the use of mathematical induction in establishing a key result that enables backpropagation for derivative amplification coefficients. Then we establish the connection between derivative amplification coefficients and error coefficients (commonly referred to as errors in the literature), and show that the same backpropagation procedure can be used for error coefficients. The entire derivation is thus rigorous, simple, and elegant.