Empirical design of experiments using neural network models
First Claim
1. A method for a design of experiments for modeling the effects of two or more input variables on one or more output variables, the method comprising the steps of:
- (a) generating a data set comprising data points from historical data for the input variables and the output variables, each data point comprising corresponding values for one or more of the input variables and one or more of the output variables from the historical data;
(b) identifying fault data points in the historical data, a fault data point being a data point from the data set from the historical data in which an output variable value is determined to be caused by factors other than the input variables;
(c) removing the identified fault data points from the data set, thereby generating a revised data set with no fault data points, a no fault data point being a data point from the data set from the historical data that is not determined to be a fault data point;
(d) supplying the no fault data points from the revised data set into a nonlinear neural network model; and
(e) deriving a simulator model characterizing a relationship between the input variables and the output variables using the nonlinear neural network model with the supplied data.
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Abstract
Methods and apparatus are provided pertaining to a design of experiments. The method comprises generating a data set from historical data; identifying and removing any fault data points in the data set so as to create a revised data set; supplying the data points from the revised data set into a nonlinear neural network model; and deriving a simulator model characterizing a relationship between the input variables and the output variables. The apparatus comprises means for generating a data set from historical data; means for identifying and removing any fault data points in the data set so as to create a revised data set; means for supplying the data points from the revised data set into a nonlinear neural network model; and means for deriving a simulator model characterizing a relationship between the input variables and the output variables.
52 Citations
24 Claims
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1. A method for a design of experiments for modeling the effects of two or more input variables on one or more output variables, the method comprising the steps of:
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(a) generating a data set comprising data points from historical data for the input variables and the output variables, each data point comprising corresponding values for one or more of the input variables and one or more of the output variables from the historical data; (b) identifying fault data points in the historical data, a fault data point being a data point from the data set from the historical data in which an output variable value is determined to be caused by factors other than the input variables; (c) removing the identified fault data points from the data set, thereby generating a revised data set with no fault data points, a no fault data point being a data point from the data set from the historical data that is not determined to be a fault data point; (d) supplying the no fault data points from the revised data set into a nonlinear neural network model; and (e) deriving a simulator model characterizing a relationship between the input variables and the output variables using the nonlinear neural network model with the supplied data. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A method for generating an enhanced algorithm for representing the effects of two or more input variables on an output variable, comprising:
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(a) generating an original algorithm for modeling the output variables as a function of one or more of the input variables; (b) generating a data set comprising data points from historical data for the input variables and the output variables, each data point comprising corresponding values for one or more of the input variables and one or more of the output variables; (c) identifying fault data points in the historical data, a fault data point being a data point in which an output variable value is determined to be caused by factors other than the input variables; (d) removing the identified fault data points from the data set, thereby generating a revised data set with data points; (e) supplying the data points from the revised data set into a nonlinear neural network model; (f) deriving a simulator model characterizing a relationship between the input variables and the output variables using the nonlinear neural network model with the supplied data; (g) selecting a plurality of statistical measures to characterize the data points with respect to each of the input variables in the revised data set; (h) determining statistical measure values for each statistical measure for each of the input variables, based on the data points in the revised data set; (i) supplying the statistical measure values into the derived simulator model; (j) determining calculated values from the derived simulator model of one or more of the output variables corresponding to the respective statistical measure values of the input variables; (k) selecting dominant input variables, each of the dominant input variables being the input variables having dominant effects on the output variables; and (l) generating an enhanced algorithm for modeling the output variables as a function of one or more of the dominant input variables. - View Dependent Claims (11, 12, 13, 14, 15, 16, 17)
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18. A system for modeling the effects of two or more input variables on an output variable comprising:
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(a) means for generating a data set comprising data points from historical data for the input variables and the output variables, each data point comprising corresponding values for one or more of the input variables and one or more of the output variables; (b) means for identifying fault data points in the historical data, a fault data point being a data point in which an output variable value is determined to be caused by factors other than the input variables; (c) means for removing the identified fault data points from the data set, thereby generating a revised data set with data points; (d) means for supplying the data points from the revised data set into a nonlinear neural network model; and (e) means for deriving a simulator model characterizing a relationship between the input variables and the output variables using the nonlinear neural network model with the supplied data. - View Dependent Claims (19, 20, 21)
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22. A program product comprising:
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(a) a program configured to at least facilitate; (i) generating a data set comprising data points from historical data for the input variables and the output variables, each data point comprising corresponding values for one or more of the input variables and one or more of the output variables from the historical data; (ii) identifying fault data points in the historical data, a fault data point being a data point from the data set from the historical data in which an output variable value is determined to be caused by factors other than the input variables; (iii) removing the identified fault data points from the data set, thereby generating a revised data set with no fault data points, a no fault data point being a data point from the data set from the historical data that is not determined to be a fault data point; (iv) supplying the no fault data points from the revised data set into a nonlinear neural network model; and (v) deriving a simulator model characterizing a relationship between the input variables and the output variables using the nonlinear neural network model with the supplied data; and (b) a computer-readable signal bearing media bearing the program.
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23. An apparatus comprising:
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(a) a processor; (b) a memory coupled to the processor; and (c) a program residing in memory and being executed by the processor, the program configured to characterize a relationship between one or more input variables and one or more output variables through at least the following steps; (i) generating a data set comprising data points from historical data for the input variables and the output variables, each data point comprising corresponding values for one or more of the input variables and one or more of the output variables from the historical data; (ii) identifying fault data points in the historical data, a fault data point being a data point from the data set from the historical data in which an output variable value is determined to be caused by factors other than the input variables; (iii) removing the identified fault data points from the data set, thereby generating a revised data set with no fault data points, a no fault data point being a data point from the data set from the historical data that is not determined to be a fault data point; (iv) supplying the no fault data points from the revised data set into a nonlinear neural network model; and (v) deriving a simulator model characterizing a relationship between the input variables and the output variables using the nonlinear neural network model with the supplied data. - View Dependent Claims (24)
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Specification