A method for controlling a turbine is proposed, which is characterized at any point in the control by a hidden state. The dynamic behavior of the turbine is modeled with a recurrent neural network comprising a recurrent hidden layer. In this case, the recurrent hidden layer is formed from vectors of neurons, which describe the hidden state of the turbine at the time points of the regulation, wherein two vectors are chronologically linked for each time point with a first connection bridging a time and second connection bridging at least two points in time. Short-term effects can be controlled by means of the first connections and long-term effects can be adjusted by means of the second connections. Secondly, emissions and also occurring dynamics in the turbine can be minimized. Furthermore, a regulating device and a turbine with such a regulating device are proposed.