DATA-DRIVEN APPROACH TO MODELING SENSORS
First Claim
1. A method for optimizing a system, comprising:
- receiving one or more historical values for each of a plurality of system parameters;
grouping the system parameters into controllable parameters, non-controllable parameters, and performance parameters;
determining a first set of predictors from the non-controllable parameters using the historical values of these non-controllable parameters;
for each predictor in the first set of predictors, determining one or more optimal time instances at which a value of each predictor in the first set of predictors is measured using non-uniform time scales;
storing the optimal time instances for each predictor in the first set of predictors as a second set of predictors;
establishing one or more constraints for each of the controllable parameters;
constructing a dynamic model based on the second set of predictors, the controllable parameters, and the performance parameters; and
optimizing the dynamic model with a non-gradient-based algorithm.
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Accused Products
Abstract
A method, computer program product and system are provided for modeling non-controllable parameters affecting system performance. The method may include receiving historical values for each of a plurality of system parameters and grouping the system parameters into controllable, non-controllable, and performance parameters. The method may further include determining a first set of predictors from the non-controllable parameters using the historical values of these non-controllable parameters and, for each predictor in the first set, determining optimal time instances at which a value of each predictor is measured using non-uniform time scales. These optimal time instances may then be saved as a second set of predictors. One or more constraints may then be established for each of the controllable parameters. Finally, a dynamic model based on the second set of predictors, the controllable parameters, and the performance parameters may be constructed and optimized.
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Citations
20 Claims
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1. A method for optimizing a system, comprising:
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receiving one or more historical values for each of a plurality of system parameters; grouping the system parameters into controllable parameters, non-controllable parameters, and performance parameters; determining a first set of predictors from the non-controllable parameters using the historical values of these non-controllable parameters; for each predictor in the first set of predictors, determining one or more optimal time instances at which a value of each predictor in the first set of predictors is measured using non-uniform time scales; storing the optimal time instances for each predictor in the first set of predictors as a second set of predictors; establishing one or more constraints for each of the controllable parameters; constructing a dynamic model based on the second set of predictors, the controllable parameters, and the performance parameters; and optimizing the dynamic model with a non-gradient-based algorithm. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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13. A computer program product for optimizing a system, said computer program product comprising at least one computer-readable storage medium having computer-readable program code portions stored therein, wherein the computer-readable program code portions comprise:
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a first executable portion for receiving one or more historical values for each of a plurality of system parameters; a second executable portion for grouping the system parameters into controllable parameters, non-controllable parameters, and performance parameters; a third executable portion for determining a first set of predictors from the non-controllable parameters using the historical values of these non-controllable parameters; a fourth executable portion for determining, for each predictor in the first set of predictors, one or more optimal time instances at which a value of each predictor in the first set of predictors is measured using non-uniform time scales; a fifth executable portion for storing the optimal time instances for each predictor in the first set of predictors as a second set of predictors; a sixth executable portion for establishing one or more constraints for each of the controllable parameters; a seventh executable portion for constructing a dynamic model based on the second set of predictors, the controllable parameters, and the performance parameters; and an eighth executable portion for optimizing the dynamic model with a non-gradient-based algorithm. - View Dependent Claims (14, 15, 16, 17, 18, 19)
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20. A system comprising:
a processor configured to; receive one or more historical values for each of a plurality of system parameters; group the system parameters into controllable parameters, non-controllable parameters, and performance parameters; determine a first set of predictors from the non-controllable parameters using the historical values of these non-controllable parameters; for each predictor in the first set of predictors, determine one or more optimal time instances at which a value of each predictor in the first set of predictors is measured using non-uniform time scales; store the optimal time instances for each predictor in the first set of predictors as a second set of predictors; establish one or more constraints for each of the controllable parameters; construct a dynamic model based on the second set of predictors, the controllable parameters, and the performance parameters; and optimize the dynamic model with a non-gradient-based algorithm.
Specification