Abstract: A method for the computer-assisted control and/or regulation of a technical system is provided. The method is used to efficiently reduce a high-dimensional state space describing the technical system to a smaller dimension. The reduction of the state space is performed using an artificial recurrent neuronal network. In addition, the reduction of the state space enables conventional learning methods, which are only designed for small dimensions of state spaces, to be applied to complex technical systems with an initially large state space, wherein the conventional learning methods are performed in the reduced state space. The method can be used with any technical system, especially gas turbines.

Abstract: A method for the computer-assisted control and/or regulation of a technical system is provided. The method includes two steps, namely modeling the dynamic behavior of the technical system with a recurrent neural network using training data, the recurrent neural network includes states and actions determined using a simulation model at different times and learning an action selection rule by the recurrent neural network to a further neural network. The method can be used with any technical system in order to control the system in an optimum computer-assisted manner. For example, the method can be used in the control of a gas turbine.

Abstract: A method for the computer-assisted exploration of states of a technical system is provided. The states of the technical system are run by carrying out an action in a respective state of the technical system, the action leading to a new state. A safety function and a feedback rule are used to ensure that a large volume of data of states and actions is run during exploration and that at the same time no inadmissible actions occur which could lead directly or indirectly to the technical system being damaged or to a defective operating state. The method allows a large number of states and actions relating to the technical system to be collected and may be used for any technical system, especially the exploration of states in a gas turbine. The method may be used both in the real operation and during simulation of the operation of a technical system.

Abstract: A method for computer-aided control of any technical system is provided. The method includes two steps, the learning of the dynamic with historical data based on a recurrent neural network and a subsequent learning of an optimal regulation by coupling the recurrent neural network to a further neural network. The recurrent neural network has a hidden layer comprising a first and a second hidden state at a respective time point. The first hidden state is coupled to the second hidden state using a matrix to be learned. This allows a bottleneck structure to be created, in that the dimension of the first hidden state is smaller than the dimension of the second hidden state or vice versa. The autonomous dynamic is taken into account during the learning of the network, thereby improving the approximation capacity of the network. The technical system includes a gas turbine.

Abstract: A method for the computer-assisted exploration of states of a technical system is provided. The states of the technical system are run by carrying out an action in a respective state of the technical system, the action leading to a new state. A safety function and a feedback rule are used to ensure that a large volume of data of states and actions is run during exploration and that at the same time no inadmissible actions occur which could lead directly or indirectly to the technical system being damaged or to a defective operating state. The method allows a large number of states and actions relating to the technical system to be collected and may be used for any technical system, especially the exploration of states in a gas turbine. The method may be used both in the real operation and during simulation of the operation of a technical system.

Abstract: A method for the computer-assisted control and/or regulation of a technical system is provided. The method includes two steps, namely learning the dynamics of a technical system using historical data based on a recurrent neuronal network, and the subsequent learning of an optimum regulation by coupling the recurrent neuronal network to another neuronal network. The method can be used with any technical system in order to control the system in an optimum computer-assisted manner. For example, the method can be used in the control of a gas turbine.

Abstract: A method for the computer-assisted control and/or regulation of a technical system is provided. The method is used to efficiently reduce a high-dimensional state space describing the technical system to a smaller dimension. The reduction of the state space is performed using an artificial recurrent neuronal network. In addition, the reduction of the state space enables conventional learning methods, which are only designed for small dimensions of state spaces, to be applied to complex technical systems with an initially large state space, wherein the conventional learning methods are performed in the reduced state space. The method can be used with any technical system, especially gas turbines.