An algebraic proof for backpropagation in acyclic neural networks

All effort is made to construct a direct algebraic proof for backpropagation in acyclic neural networks. This result would provide neural network designers with more flexibility in their choice of network architecture. Specifically, it is proved that a specified acyclic backpropagation net minimizes the mean square sum of the error values of the all processing units