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Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources
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5.2. ResultsofSystemIdentification 0 2 0 0 4 0 0 6 0 0 8 0 0 1 0 0 00 5 1 0 1 5 2 0 T i m e ( s ) E K FB P MS W B P (b)Comparisonof thestandarddeviation. Figure5.16. Validationresultsof threealgorithms. Algorithm LearningMSE MeanSDof validation Time(per update) EKF 0.1172 0.7743 0.0031s BPM 0.0990 9.8806 0.0015s SWBP 0.0974 10.4665 0.3446s Table5.2. Performancecomparisonof threeonlineNNtrainingalgorithms. to the highest estimation accuracy and more comprehensive perfor- mance, the EKF algorithm is applied in the NNC system to train the systemestimator. Performance of both grey-box and black-box approaches can be com- pared qualitatively according to above results. On the one hand, both linearandnonlinearon-linerecursivesystemidentificationalgorithms havefastconvergingspeedthantheNNapproaches. Butontheother 153
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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