Inferential sensors developed using three-dimensional pareto-front genetic programming
First Claim
1. A method of developing a predictive algorithm for predicting at least one output variable based on a plurality of input variables, said method comprising:
- obtaining a first set of data representative of a physical, chemical, or biological process, said first set of data including first-set measurements of said at least one output variable and corresponding first-set measurements of said input variables;
evolving a plurality of candidate algorithms using a genetic programming technique that applies at least three fitness criteria, said at least three fitness criteria including an accuracy criterion that evaluates each candidate algorithm'"'"'s ability to predict said first-set measurements of said at least one output variable based on said corresponding first-set measurements of said input variables, a complexity criterion that evaluates each candidate algorithm'"'"'s complexity, and a smoothness criterion that evaluates each candidate algorithm'"'"'s nonlinearity; and
selecting one of said candidate algorithms as said predictive algorithm.
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Abstract
A predictive algorithm for predictive at least one output variable based on a plurality of input variables is developed using a genetic programming technique that evolves a population of candidate algorithms through multiple generations. Within each generation, the candidate algorithms are evaluated based on three fitness criteria: (i) an accuracy criterion that evaluates each candidate algorithm'"'"'s ability to predict historical measurements of the at least one output variable based on corresponding historical measurements of the input variables; (ii) a complexity criterion that evaluates each candidate algorithm'"'"'s complexity; and (iii) a smoothness criterion that evaluates each candidate algorithm'"'"'s nonlinearity. The predictive algorithm may be implemented in an inferential sensor that is used to monitor a physical, chemical, or biological process, such as an industrial process in an industrial plant.
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Citations
20 Claims
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1. A method of developing a predictive algorithm for predicting at least one output variable based on a plurality of input variables, said method comprising:
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obtaining a first set of data representative of a physical, chemical, or biological process, said first set of data including first-set measurements of said at least one output variable and corresponding first-set measurements of said input variables; evolving a plurality of candidate algorithms using a genetic programming technique that applies at least three fitness criteria, said at least three fitness criteria including an accuracy criterion that evaluates each candidate algorithm'"'"'s ability to predict said first-set measurements of said at least one output variable based on said corresponding first-set measurements of said input variables, a complexity criterion that evaluates each candidate algorithm'"'"'s complexity, and a smoothness criterion that evaluates each candidate algorithm'"'"'s nonlinearity; and selecting one of said candidate algorithms as said predictive algorithm. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A method of monitoring a physical, chemical, or biological process, said method comprising:
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determining current values of a plurality of measurable parameters of said physical, chemical, or biological process; providing said current values to an inferential sensor that is configured to use at least one predictive algorithm that predicts an additional parameter of said physical, chemical, or biological process based on said measurable parameters, said at least one predictive algorithm having been optimized with respect to accuracy, complexity, and smoothness; and said inferential sensor using said at least one predictive algorithm to determine an inferred value of said additional parameter based on said current values of said measurable parameters. - View Dependent Claims (11, 12, 13, 14, 15, 16, 17, 18)
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19. A system for monitoring a physical, chemical, or biological process, said system comprising:
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at least one hardware sensor for obtaining measured values of a plurality of parameters of said physical, chemical, or biological process; and an inferential sensor for determining an inferred value of an additional parameter of said physical, chemical, or biological process based on said measured values, said inferential sensor being configured to determine said inferred value using at least one predictive algorithm that has been optimized with respect to accuracy, complexity, and smoothness using a genetic programming technique. - View Dependent Claims (20)
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Specification