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Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources
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3.2. Grey-boxModeling caused by different thermal radiation effects can be omitted, es- pecially when the temperature is in a stable range. Then then-th MISOsystemscanbewrittenas Yn(k) =Anct ·Yn(k−1)+Ψn(k−1), Ψn(k−1) =VT(k−1)[Φn(k−1)]V(k−1). (3.64) Atanytimek, thevalueofΨn(k−1)canbecalculatedby Ψn(k−1) =Yn(k)−Anct ·Yn(k−1), (3.65) and then the effective heating matrix [Φn(k−1)] can be estimated accordingto thevalueofΨn(k−1). As inthelinearRKF(equation 3.59), inEKFit isalsoassumedthat thesystemfulfills the followingequations Ψnr(k−1) =VT(k−1)[Φn(k−1)]V(k−1)+ ς(k−1), [Φn(k−1)] = [Φn(k−2)]+[ε(k−1)], (3.66) whereΨnr(k−1) is therealcalculatedvalueofΨn(k−1), ς(k−1) is azero-meanmeasurementorcalculationerrorwiththecovariance σ2 and [ε(k−1)] is a zero-mean white noise with the covariance matrix [Ω]. The definitions of parameters and vectors used in EKF isshownbytable 3.2. The detailed derivation process is enclosed in the appendix A.1. TheupdateruleofEKFisgivenas following. Predictionpartwithk≥2 :[ Φnp(k−1) ] = [Φne(k−2)] ,[ Pnp(k−1) ] = [Pne(k−2)]+[Ω]. (3.67) 63
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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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Technik
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Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources