Zero dynamics and relative degree of dynamic recurrent neural networks

In this paper the differential geometric control theory is used to define the key concepts of relative degree and zero dynamics for a Dynamic Recurrent Neural Network (DRNN). It is shown that the relative degree is the lower bound for the number of neurons and the zero dynamics are responsible for the approximating capabilities of the network.