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Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources
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3. ModelingMicrowaveHeating Thenthere is∥∥∥−→En∥∥∥2=EnTEn = Enx 2+Eny 2+Enz 2 = ( EnxV )T EnxV+ ( EnyV )T EnyV+ ( EnzV )T EnzV =VTEnx TEnxV+V TEny TEnyV+V TEnz TEnzV =VT ( Enx TEnx+E n y TEny+E n z TEnz ) V (3.26) The original microwave heating power Pnmw can be rewritten using equation 3.26as Pnmw= 1 2 l3σe(T) · ∥∥∥−→En∥∥∥2 = 1 2 l3σe(T) ·VT ( Enx TEnx+E n y TEny+E n z TEnz ) V =VT [Φnc(T)]V, (3.27) where thematrix [Φnc(T)] isdefinedby [Φnc(T)] := 1 2 l3σe(T) · ( Enx TEnx+E n y TEny+E n z TEnz ) . (3.28) The matrix [Φnc(T)] is aM×M symmetric square matrix, which is the effective heating matrix at this location n, and V is the control input vector that has to be calculated by a controller. In contrast to thecaseofscalarsuperposition, thevectorsuperpositionrule ismuch more complicated due to its high computation complexity. However, it is accurate and effective to describe the power superposition of mi- crowave heating systems with multiple feeding sources, as shown by thesimulationresults in [BPJD99]and[THN01]. 3.2.3. FormulationandDiscretization In this section, above approximations 3.15, 3.18 and 3.27 will be further formulated and the resulted equations will be discretized, to 50
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Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources
Title
Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources
Author
Yiming Sun
Publisher
KIT Scientific Publishing
Location
Karlsruhe
Date
2016
Language
English
License
CC BY-SA 3.0
ISBN
978-3-7315-0467-2
Size
14.8 x 21.0 cm
Pages
260
Keywords
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