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Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources
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5.1. Verification inHEPHAISTOS cal. Thesameresultsarealsoreflectedbytheheatingratesas infigure 5.5. 1 2 3 4 50 .0 0 .1 0 .2 0 .3 0 .4 0 .5 0 .6 0 .7 0 .8 I n d e x o f m e a s u r e d p o i n t s T r i a l 1 T r i a l 2 T r i a l 3 Figure5.5. Heatingratescomparisonof3 trialsduringthefirst twoseconds. The heating rates shown in figure 5.5 are calculated using the follow- ingequation R= 1 ∆t ( Y(k+1)−A(k)Y(k)) ∝ Pmw. (5.1) Since the heating rates are directly proportional to the microwave heating power, therefore they can be used as representatives of the heatingpower. Inotherwords, if theheatingratesofdifferentsources follow the scalar addition rule, it means the heating power also fol- lows the scalar addition rule. This principle will be used later in the thirdstepof theverification. Step2: Heatingpatternsofmultiplefeedingsources The second step is to test the heating stability of multiple feeding sources. The proceduresused in this setupis thesame asdescribed in figure 5.2, except the objective is switched to different combinations 137
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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