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Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources
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A. Appendix fo(z o j(k)) =z o j(k), thecompletedupdateequation is woj,i(k+1) =w o j,i(k)−η ·δoj(k)xLi (k), =woj,i(k)−η ·δoj(k)xLi (k). (B.23) Case2: Forweightsonlyconnectingtohiddenlayers N1,1 N1,2 N2,1 N2,2 N2,3 N3,1 Input Layer Hidden Layer Output Layer YNN,1 u1(k) u2(k) W13,4 Bias = 1 N3,2 YNN,2 W22,4 Bias = 1 W11,2 FigureB.2. Errorflowsfromhiddenlayers to theoutput layer. When both ends of the link are connected to nodes in hidden layers, the update of its weight becomes complicated. There is no specified desiredoutputfornodesinhiddenlayerstodirectlycalculatetheerror( YNN,j(k)−Yd,j(k) ) . Inaddition,allnodes in theoutput layerhaveto be considered because the error flows from the node in the hidden layer to all nodes in the output layer, such as indicated by the blue arrows infigure B.2. Using a similar equation as in the first case to calculate ∆woj,i(k), for theL-th layer there is ∆wLj,i(k) =−η ∂J(k) ∂wLj,i(k) = −η ∂J(k) ∂zLj (k) ∂zLj (k) ∂wLj,i(k) =−η ·δLj (k)xL−1i (k). (B.24) 230
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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