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Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources
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5.1. Verification inHEPHAISTOS 1 2 3 4 5 6 7 8 9 1 0 1 1 1 2 1 3 1 4 1 5 1 60 .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 R e a l h e a t i n g r a t eL i n e a r s u p e r p o s e d h e a t i n g r a t (a)Resultof4 feedingsources (No. 1−4). 1 2 3 4 5 6 7 8 9 1 0 1 1 1 2 1 3 1 4 1 5 1 60 .0 0 .2 0 .4 0 .6 0 .8 1 .0 1 .2 1 .4 1 .6 1 .8 I n d e x o f m e a s u r e d p o i n t s R e a l h e a t i n g r a t eL i n e a r s u p e r p o s e d h e a t i n g r a (b)Resultsof8 feedingsources (No. 1−8). Figure5.11. Comparison between real and linear superposed heating rates of4(a)or8(b) feedingsourcesat16differentpoints. when the number of feeding sources is low, particularly for cases wheresourcesare fromdifferentmodules. After above 3 steps, assumptions used in the grey-box modeling have been verified and guaranteed to be accurate to a great extent. In addition, heating patterns and corresponding heating rates obtained from the verification experiments are quite helpful in the following 143
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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