Training a support vector machine with process constraints
First Claim
1. A method, comprising:
- providing a model, wherein the model comprises a representation of a plant or process implemented with a support vector machine (SVM), wherein the model comprises one or more inputs and one or more outputs, and wherein the plant or process has one or more known attributes;
specifying one or more process constraints that correspond to the one or more known attributes; and
training the model subject to the one or more process constraints.
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Abstract
System and method for training a support vector machine (SVM) with process constraints. A model (primal or dual formulation) implemented with an SVM and representing a plant or process with one or more known attributes is provided. One or more process constraints that correspond to the one or more known attributes are specified, and the model trained subject to the one or more process constraints. The model includes one or more inputs and one or more outputs, as well as one or more gains, each a respective partial derivative of an output with respect to a respective input. The process constraints may include any of: one or more gain constraints, each corresponding to a respective gain; one or more Nth order gain constraints; one or more input constraints; and/or one or more output constraints. The trained model may then be used to control or manage the plant or process.
48 Citations
25 Claims
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1. A method, comprising:
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providing a model, wherein the model comprises a representation of a plant or process implemented with a support vector machine (SVM), wherein the model comprises one or more inputs and one or more outputs, and wherein the plant or process has one or more known attributes;
specifying one or more process constraints that correspond to the one or more known attributes; and
training the model subject to the one or more process constraints. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22)
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23. A computer-accessible memory medium, wherein the memory medium comprises program instructions executable to:
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implement a model with a support vector machine (SVM), wherein the model comprises one or more inputs and one or more outputs, wherein the model comprises a representation of a plant or process, and wherein the plant or process has one or more known attributes;
specify one or more process constraints, wherein each of the one or more process constraints corresponds to a respective known attribute; and
training the model subject to the one or more process constraints.
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24. A system, comprising:
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means for providing a model, wherein the model comprises representation of a plant or process implemented with a support vector machine (SVM), wherein the model comprises one or more inputs and one or more outputs, and wherein the plant or process has one or more known attributes;
means for specifying one or more process constraints that correspond to the one or more known attributes; and
means for training the model subject to the one or more process constraints.
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25. A system, comprising:
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a model, representing a plant or process, wherein the model is implemented by a support vector machine (SVM), and wherein the plant or process has one or more known attributes, the model comprising;
one or more inputs;
one or more outputs; and
an optimizer, coupled to the model;
wherein the model is constrained by one or more process constraints that correspond to the one or more known attributes; and
wherein the optimizer is operable to train the model subject to the one or more process constraints.
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Specification