Comparison of Global Nonlinear Models and “Model-on-Demand” Estimation Applied to Identification of a RTP Wafer Reactor
نویسندگان
چکیده
“Model on Demand” (MoD) simulation of the temperature dynamics in a simulated Rapid Thermal Processing (RTP) reactor is compared against various types of global models (ARX, semiphysical, combined semiphysical with neural net). The identification data is generated from a m-level pseudo-random sequence input whose parameters are specified 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 effort and simplified a priori knowledge regarding model structure.
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