期刊
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
卷 13, 期 1, 页码 169-179出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TEVC.2008.920680
关键词
Data mining; evolutionary strategy; model predictive control; nonlinear temporal process; optimization
资金
- Iowa Energy Center [IEC 04-06]
- Div Of Chem, Bioeng, Env, & Transp Sys
- Directorate For Engineering [0835607] Funding Source: National Science Foundation
A dynamic predictive-control model of a nonlinear and temporal process is considered. Evolutionary computation and data mining algorithms tire integrated for solving the model. Data-mining algorithms learn dynamic equations from process data. Evolutionary algorithms are then applied to solve the optimization problem guided by the knowledge extracted by data-mining algorithms. Several properties of the optimization model are shown in detail, in particular, a selection of regressors, time delays, prediction and control horizons, and weights. The concepts proposed in this paper are illustrated with an industrial case study in combustion process.
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