Real-time optimal parametric design using the assess-predict-optimize strategy
For most optimization problems, the uncertainty in the output results due to errors arising from the estimation of the system parameters used in the optimization is usually difficult to measure. This thesis presents a strategy which combines the ability to approximate the experimental errors and to...
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| Format: | Thesis |
| Language: | English |
| Published: |
2014
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| Online Access: | http://hdl.handle.net/10356/60693 |
| Summary: | For most optimization problems, the uncertainty in the output results due to errors arising from the estimation of the system parameters used in the optimization is usually difficult to measure. This thesis presents a strategy which combines the ability to approximate the experimental errors and to evaluate the system parameters based on these errors with the ability to solve the optimization problem in the presence of these errors. The proposed strategy transforms the data into intervals using statistical methods and then uses these intervals to evaluate worst case scenarios of the constraints and objective functions of the optimization problem. The strategy employs a posteriori error estimation methods which produce bounds on the constraints and objective functions in order to obtain the best" worst case scenarios. These worst case scenarios reflect the propagation of error from the data and ensure the feasibility of the results of the optimization problems. |
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