# Can we modelize an RNN by an ANN that takes precedent output as a part of input?

Is it possible to consider an RNN as a classical feedforward neural network that just take the precedent output as a part of the input ?

What you're describing is some function $$f_1: \mathbb{R}^n \to \mathbb{R}^n$$ whereas the "conventional" RNN is more like $$f_2: \mathbb{R}^m \times \mathbb{R}^n \to \mathbb{R}^n$$, where $$\mathbb{R}^m$$ refers to the new information at each time step.