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Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources
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5.3. ResultsofDifferentControlMethods 0 2 0 0 0 4 0 0 0 6 0 0 0 8 0 0 04 0 6 0 8 0 1 0 0 T a r g e tT 1T 2T 3T 4T 5 DT = 0 . 9 ° C ~ 1 . 1 ° C DT = 1 3 . 5 ° C ~ 1 4 ° C T i m e ( s ) Figure5.36. Simulationresultsof theQ(λ)basedRLCmethod. 0 5 0 0 1 0 0 0 1 5 0 02 0 4 0 6 0 8 0 1 0 0 DT = 1 2 . 8 ° C ~ 1 6 . 5 ° C T i m e ( s ) T a r g e tT m a xT m i n DT = 1 7 . 6 ° C ~ 1 8 ° C 0 2 0 4 0 6 0 8 0 1 0 0 (a) The first trial. to 0.1 in the end of the heating process. In order to have a more stable control performance, meanwhile also keep a certain exploration abil- 179 ity, the exploration probability used the in the last two trials is set to be0.1.
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Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources
Titel
Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources
Autor
Yiming Sun
Verlag
KIT Scientific Publishing
Ort
Karlsruhe
Datum
2016
Sprache
englisch
Lizenz
CC BY-SA 3.0
ISBN
978-3-7315-0467-2
Abmessungen
14.8 x 21.0 cm
Seiten
260
Schlagwörter
Mikrowellenerwärmung, Mehrgrößenregelung, Modellprädiktive Regelung, Künstliches neuronales Netz, Bestärkendes Lernenmicrowave heating, multiple-input multiple-output (MIMO), model predictive control (MPC), neural network, reinforcement learning
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Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources