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Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources
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5. ExperimentalResults 1 2 3 4 50 .0 0 .5 1 .0 1 .5 2 .0 2 .5 3 .0 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 (d)Heatingratesof3 trials. Figure5.7. Heating patterns (a, b, c) and rates (d) of 12 sources (from 1 to 12) at3different trials. Results in 5.10 and 5.11 are just epitomes of a large scale of experi- mental data. In most cases accuracies of the linear superposed heat- ing rates are over 85%. The accuracies are even higher (over 90%) for for combinations of a large number of heating sources (more than 4). Therefore, in general the linear model 3.41 is considered as a accu- rate approximation of the practical heating scenarios. Nevertheless, ontheotherhand, incertaincasesthedifferencesbetweenrealandes- timated heating rates can be drastic. such as the results shown in the following figure 5.12. It is clear that there are 4 hot spot areas in the heating pattern of No. 3 and 7 (as illustrated by the dashed circles in figure 5.12), three of which can be interpreted by the linear superpo- sition principle. But the heating rate of the upper left corner is much higher than the linear superposed value (as index ’1’ in figure 5.12d). Infact,boththefirst twothermalpatternshavelowheatingrates(cold spot) in the upper left corner, but the pattern of the heating combina- tion has a clear hot spot in this region. Meanwhile, it is also worthy tonotice that inmanycases (includingthecaseshowninfigure 5.12), the thermal pattern of the heating combination is not exactly equiva- 140
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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