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Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources
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5.1. Verification inHEPHAISTOS of heating sources does not have any influences to this stability. Re- sults from the first and the second steps prove that HEPHAISTOS is a non-stochastic system. The dynamics of HEPHAISTOS are possible to be modeled and learned via repeated experiments and tests, which provides a solid foundation to all previous introduced modeling and controlmethods. Step3: Validityof linearandnonlinearmodels In the last verification step, the previously mentioned principle of heating rates is utilized, to verify if models 3.41 and 3.46 are accu- rate approximations of practical heating situations. In order to get a more comprehensive result, more temperatures were included in the following verification experiment such as in figure 5.8 . Correspond- ingVerificationproceduresaredescribedas infigure 5.9. It is easy to test the linear superposition rule, by comparing the lin- earlysuperposedheatingrateRlinearwiththerealoverallheatingrate Rreal. If the linear superposed heating rate Rlinear is equivalent to the real heating rateRreal, it means that the linear superposed power also equals to the practical overall heating power, and consequently, it proves the validity of the linear model 3.41 . The corresponding verification result for two different feeding sources is shown in the followingfigure 5.10 . From the above figure it is noted that the linear superposed heating rate Rlinear is close to the real heating rate Rreal. For situations with moresources, thesameresult stillholds 5.11. (a)Trial1at time t=2s (b)Trial2at time t=2s (c)Trial3at time t=2s 139
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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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Technik
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Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources