Patent ID: 7451122

Claim:
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.