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Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources
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A. Appendix N1,1 N1,2 N2,1 N2,2 N2,3 N3,1 Input Layer Hidden Layer Output Layer W11,1 YNN,1 u1(k) u2(k) W13,4 Bias = 1 N3,2 YNN,2 W22,4 Bias = 1 W22,1 FigureB.1. Errorflowintheoutput layer. A.2. DerivationofBackpropagation Algorithm Case1: Forweightsconnectingto theoutput layer If thedestinationofthelinkis intheoutput layer,suchasthebluelink in figure B.1, any error of the weight only affects one output and the corresponding weight update is simple and straightforward. For the destination node j in the output layer, according to the definition in table 3.3 its inputcanberepresentedby zoj(k) = NL∑ i=1 woj,i(k)x L i (k), (B.19) 228
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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