Method for computer-aided control and/or regulation using two neural networks wherein the second neural network models a quality function and can be used to control a gas turbine
First Claim
1. A method for computer-aided control of a technical system, comprising:
- characterizing a dynamic behavior of the technical system for a plurality of time points by a state of the technical system and an action carried out on the technical system for each time point, wherein an action at a time point results in a sequential state of the technical system at a next time point;
learning an action selection rule with a plurality of data records, wherein each data record comprises the state of the technical system at the time point, the action carried out in the state and the sequential state, and wherein an evaluation is assigned to each data record, the learning of the action selection rule comprising;
modeling of a quality function by a first neural network comprising the states and actions of the technical system as parameters;
learning the first neural network based on an optimality criterion, which is a function of the evaluations of the data records and the quality function, an optimum action in respect of the quality function being modeled by a second neural network, which is learned based on the quality function; and
controlling the technical system such that the actions to be carried out on the technical system are selected using the action selection rule based upon the second neural network.
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Abstract
A method for a computer-aided control of a technical system is provided. The method involves use of a cooperative learning method and artificial neural networks. In this context, feed-forward networks are linked to one another such that the architecture as a whole meets an optimality criterion. The network approximates the rewards observed to the expected rewards as an appraiser. In this way, exclusively observations which have actually been made are used in optimum fashion to determine a quality function. In the network, the optimum action in respect of the quality function is modeled by a neural network, the neural network supplying the optimum action selection rule for the given control problem. The method is specifically used to control a gas turbine.
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Citations
20 Claims
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1. A method for computer-aided control of a technical system, comprising:
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characterizing a dynamic behavior of the technical system for a plurality of time points by a state of the technical system and an action carried out on the technical system for each time point, wherein an action at a time point results in a sequential state of the technical system at a next time point; learning an action selection rule with a plurality of data records, wherein each data record comprises the state of the technical system at the time point, the action carried out in the state and the sequential state, and wherein an evaluation is assigned to each data record, the learning of the action selection rule comprising; modeling of a quality function by a first neural network comprising the states and actions of the technical system as parameters; learning the first neural network based on an optimality criterion, which is a function of the evaluations of the data records and the quality function, an optimum action in respect of the quality function being modeled by a second neural network, which is learned based on the quality function; and controlling the technical system such that the actions to be carried out on the technical system are selected using the action selection rule based upon the second neural network. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19)
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20. A non-transitory computer readable medium storing a program code for implementing a method for computer-aided control of a technical system when the program is running on a computer, the method comprising:
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characterizing a dynamic behavior of the technical system for a plurality of time points by a state of the technical system and an action carried out on the technical system for each time point, wherein an action at a time point results in a sequential state of the technical system at a next time point; learning an action selection rule with a plurality of data records, wherein each data record comprises the state of the technical system at the time point, the action carried out in the state and the sequential state, and wherein an evaluation is assigned to each data record, the learning of the action selection rule comprising; modeling of a quality function by a first neural network comprising the states and actions of the technical system as parameters; learning the first neural network based on an optimality criterion, which is a function of the evaluations of the data records and the quality function, an optimum action in respect of the quality function being modeled by a second neural network, which is learned based on the quality function; and controlling the technical system such that the actions to be carried out on the technical system are selected using the action selection rule based upon the second neural network.
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Specification