INITIALIZATION OF RADIAL BASE FUNCTION NEURAL NETWORK NODES FOR REINFORCEMENT LEARNING INCREMENTAL CONTROL SYSTEM
First Claim
1. A computer-implemented method for adjusting a process variable using a closed loop system, the method comprising:
- initializing a radial basis function neural network (RBF network), the initialization using a maximum error (emax), a maximum first order change in error (Δ
emax), a maximum second order change in error (Δ
2emax), and a maximum output increment (Δ
omax), associated with the closed loop system being controlled;
inputting, to the RBF network, input values comprising an error, a first order change in error, and a second order change in error;
computing, by the RBF network, control parameters based on the input values;
computing, by the RBF network, an incremental change in the process variable based on the control parameters; and
adjusting, by a controller, an output device to change the process variable by the incremental change.
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Accused Products
Abstract
According to one or more embodiments of the present invention, a computer-implemented method for adjusting a process variable using a closed loop system includes initializing a radial basis function neural network (RBF network) using a maximum error (emax), a maximum first order change in error (Δemax), a maximum second order change in error (Δ2emax), and a maximum output increment (Δomax), associated with the closed loop system being controlled. The method further includes inputting, to the RBF network, input values including an error, a first order change in error, and a second order change in error. The method further includes computing, by the RBF network, control parameters based on the input values, and computing, by the RBF network, an incremental change in the process variable based on the control parameters. The method further includes adjusting, by a controller, an output device to change the process variable by the incremental change.
12 Citations
25 Claims
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1. A computer-implemented method for adjusting a process variable using a closed loop system, the method comprising:
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initializing a radial basis function neural network (RBF network), the initialization using a maximum error (emax), a maximum first order change in error (Δ
emax), a maximum second order change in error (Δ
2emax), and a maximum output increment (Δ
omax), associated with the closed loop system being controlled;inputting, to the RBF network, input values comprising an error, a first order change in error, and a second order change in error; computing, by the RBF network, control parameters based on the input values; computing, by the RBF network, an incremental change in the process variable based on the control parameters; and adjusting, by a controller, an output device to change the process variable by the incremental change. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 13, 14)
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9. A control system for controlling a process variable of a closed loop system, the control system comprising:
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one or more sensors that measure the process variable; an output device that changes the process variable based on an input command; and a controller that generates and sends the input command to the output device, generating of the input command comprises; initializing a radial basis function neural network (RBF network), the initialization using a maximum error (emax), a maximum first order change in error (Δ
emax), a maximum second order change in error (Δ
2emax), and a maximum output increment (Δ
omax), associated with the closed loop system being controlled;inputting, to the RBF network, input values comprising an error, a first order change in error, and a second order change in error; computing, by the RBF network, control parameters based on the input values; computing, by the RBF network, an incremental change in the process variable based on the control parameters; and adjusting the output device to change the process variable by the incremental change. - View Dependent Claims (10, 11, 12)
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15. A computer program product for generating an input command for adjusting a process variable of a closed loop system, the computer program product comprising a memory device with computer executable instructions therein, the instructions when executed by a processing unit generate the input command, the generation of the input command comprising:
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initializing a radial basis function neural network (RBF network), the initialization using a maximum error (emax), a maximum first order change in error (Δ
emax), a maximum second order change in error (Δ
2emax), and a maximum output increment (Δ
omax), associated with the closed loop system being controlled;inputting, to the RBF network, input values comprising an error, a first order change in error, and a second order change in error; computing, by the RBF network, control parameters based on the input values; computing, by the RBF network, an incremental change in the process variable based on the control parameters; and adjusting an output device to change the process variable by the incremental change. - View Dependent Claims (16, 17, 18, 19, 20)
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21. A controller configured to adjust a process variable of a closed loop system to a set point, the adjustment comprising:
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initializing a radial basis function neural network (RBF network), the initialization using a maximum error (emax), a maximum first order change in error (Δ
emax), a maximum second order change in error (Δ
2emax), and a maximum output increment (Δ
omax), associated with the closed loop system being controlled;inputting, to the RBF network, input values comprising an error, a first order change in error, and a second order change in error; computing, by the RBF network, control parameters based on the input values; computing, by the RBF network, an incremental change in the process variable based on the control parameters; and adjusting an output device to change the process variable by the incremental change. - View Dependent Claims (22, 23)
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24. A control system for controlling a heat ventilating and air conditioning (HVAC) system, the control system comprising:
a controller that generates and sends an input command to the HVAC system to adjust a temperature to a set point, generating and sending of the input command comprises; initializing a radial basis function neural network (RBF network), the initialization using a maximum error (emax), a maximum first order change in error (Δ
emax), a maximum second order change in error (Δ
2emax), and a maximum temperature increment (Δ
omax), associated with the HVAC system being controlled;inputting, to the RBF network, input values comprising an error, a first order change in error, and a second order change in error; computing, by the RBF network, control parameters of the HVAC system based on the input values; computing, by the RBF network, an incremental change in the temperature based on the control parameters; and adjusting a temperature setting of the HVAC system to change the temperature by the incremental change. - View Dependent Claims (25)
Specification