Patent Document ID: 9639070
Application ID: 14396337
Patent Flag: 1

Claim One:
1. A method for controlling a turbine, the turbine comprising a plurality of sensors for providing sensor values ascertained on the turbine and a plurality of actuators for actuating the turbine, the turbine being characterized at each instant from a plurality of chronological instants of control by a hidden state that is derivable by sensor values and a rating signal for the hidden state and is influenceable by alterable actuator values for the plurality of actuators, the method comprising: modeling dynamic behavior of the turbine with a recurrent neural network comprising an input layer, a recurrent hidden layer, and an output layer based on training data comprising sensor values of the plurality of sensors, actuator values of the plurality of actuators, and rating signals, wherein the input layer is formed from first vectors of neurons that describe sensor values, actuator values, or sensor values and actuator values at the instants, wherein the recurrent hidden layer is formed from second vectors of neurons that describe the hidden state of the turbine at the instants, wherein chronologically, for all the instants, two respective vectors from the second vectors are connected to a first connection that spans one instant, and, chronologically, two respective vectors from the second vectors are connected to a second connection that spans at least two instants, and wherein the output layer is formed from at least one third vector of neurons that describe the rating signal or at least one portion of the sensor values, at least one portion of the actuator values, or the at least one portion of the sensor values and the at least one portion of the actuator values at the instants; performing a learning method, an optimization method, or a learning and optimization method on the hidden states in order to provide a set of rules having optimized actuator values for each of the hidden states; ascertaining the current hidden state using the recurrent neural network and currently ascertained sensor values from the plurality of sensors; ascertaining current actuator values; and actuating the plurality of actuators using the provided set of rules and the current hidden state.