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Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources
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3.3. Black-boxModeling Definition Interpretation Nl Numberofnodes in the l-th hiddenlayer (1≤ l≤L) xli(k) Outputof the i-thnode in the l-th layerat timek zl+1j (k) = ∑Nl i=1w l j,i(k) ·xli(k) Inputof the j-thnode in the (l+1)-th layerat timek δlj(k) = ∂JOL(k) ∂zlj(k) Localgradientof the j-thnode in the l-th layerat timek fo, f ′ o(z) := df′o dz Activationfunctionusedin the output layeranditsfirst derivative g, g′(z) := dg ′ dz Activationfunctionusedin the hiddenlayersanditsfirst derivative Table3.3. Parametersusedin theupdateofweights (inNN). ThevaluesYNN,i(k)andYd,i(k)arethe i-thelementfromthevector YNN(k)andYd(k), respectively. For the l-th hidden layer (1≤ l≤L), the corresponding local gra- dientvector is [Hay98][ δl(k) ] = [ δl1(k), δ l 2(k), . . . δ l Nl (k) ] , (3.80) where theelement isdefinedas δli(k) = g ′(zli(k)) · Nl+1∑ j=1 δl+1j (k)w l+1 j,i (k−1), 1≤ i≤Nl. (3.81) 79
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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