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Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources
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5. ExperimentalResults of heating sources. The verification results can be found in fi gures 5.6 and 5.7. Since the transient heating rates are more relevant to the prac- tical applications, only results about the short time heating patterns (t = 2 s) are demonstrated and analyzed in the following. (a)Trial1at time t=2s (b)Trial2at time t=2s (c)Trial3at time t=2s 1 2 3 4 50 .0 0 .2 0 .4 0 .6 0 .8 1 .0 1 .2 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. Figure5.6. Heating patterns (a, b, c) and rates (d) of source No. 1 and 5 at 3 different trials. The results in figures 5.6 and 5.7 both indicate that even multiple heating sources are switched on simultaneously, the corresponding superposed heating patterns are still stable. Meanwhile, the number 138
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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