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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
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