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- Braun, M.W., et al.
(författare)
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Multi-Level Pseudo-Random Signal Design and 'Model-on-Demand' Estimation Applied to Nonlinear Identification of a RTP Wafer Reactor
- 1999
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Ingår i: Preceedings of the 1999 American Control Conference. - Linköping : Linköping University Electronic Press. - 0780349903 ; , s. 1573-1579 vol.3
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Guidelines are presented for specifying the design parameters of multi-level pseudo-random sequences in a manner useful for “plant-friendly” nonlinear system identification. These multi-level signals are introduced into a rapid thermal processing wafer reactor simulation and compared against a well-designed pseudo-random binary sequence (PRBS). The resulting data serves as a database for a “model on demand” (MoD) predictor. MoD estimation is attractive because it requires less engineering effort to model a nonlinear plant, compared to global nonlinear models such as neural networks. The improved fit of multi-level signals over the PRBS signal, as well as the usefulness of the MoD estimator, is demonstrated on validation data.
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2. |
- Stenman, Anders, et al.
(författare)
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Comparison of Global Nonlinear Models and "Model-on-Demand" Estimation Applied to Identification of a RTP Wafer Reactor
- 1999
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Ingår i: Preceedings of the 38th IEEE Conference on Decision and Control. - Linköping : Linköping University Electronic Press. - 0780352505 ; , s. 3950-3955 vol.4
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Konferensbidrag (refereegranskat)abstract
- "Model on Demand" (MoD) simulation of the temperature dynamics in a simulated Rapid Thermal Process-ing (RTP) reactor is compared against various types of global models (ARX, semiphysical, combined semiphysical with neural net). The identication data is generated from a m-level pseudo-random sequence input whose parameters are specied systematically using a priori information readily available to the engineer. The MoD estimator outperforms the ARX model and two semi-physical models, while matching the performance of a combined semi-physical with neural net model. This makes MoD estimation an appealing alternative to global methods because of its reduced engineering eort and simplified a priori knowledge regarding model structure.
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