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Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources
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3. ModelingMicrowaveHeating trainingsetDas inclassicalbatch learning, thebasic idea is thatat eachtimek, theformerNsdatapairs(slidingwindow)areusedfor thetraining(fromtimek−Ns+1 to thecurrent timek). Therefore thecost function ischangedinto JSW(k) = 1 2Ns k∑ q=k−Ns+1 ( Y(q)−YNN(q) )T( Y(q)−YNN(q) ) . (3.89) Ateachtimek,notonlythecurrentobserveddatapairbutalsoNs former data pairs are included in this cost function JSW(k). The similar idea is widely used in neural network based time series predictions, suchas in [FDH01]. These three algorithms are all fast and efficient online NN training algorithms that are implemented in practical applications. They have been tested and compared in real experiments of HEPHAISTOS, and correspondingresultscanbefoundinchapter 5. Furtherdetailsabout the neural network design and implementations refer to [HDB+96], [Roj96], [Hay98]and[AN15]. 82
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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