Is there a neural network that accepts both the current input and previous output? I am quite new to neural networks. I am trying to implement in Python a neural network having only one hidden layer with $N$ neurons and $1$ output layer.
The point is that I am analyzing time series and would like to use the output layer as the input of the next unit: by feeding the network with the input at time $t-1$ I obtain the output $O_{t-1}$ and, in the next step, I would like to use both the input at time $t$ and $O_{t-1}$, introducing a sort of auto-regression. I read that recurrent neural network are suitable to address this issue.
Anyway I cannot imagine how to implement a network in Keras that involves multilayer recurrence: all the references I found are linked to using the output of a layer as input of the same layer in the next step. Instead, I would like to include the output of the last layer (the output layer) in the inputs of the first hidden layer.
You want to look at recurrent neural networks.