Page - 102 - in Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources
Image of the Page - 102 -
Text of the Page - 102 -
4. ControlSystemDesign
Plant
(HEPHAISTOS)
NN Controller
NN Estimator
Target
Temperature
Yt Measured
Temperature
Yr
Control
Input
U
Estimated Weights
Vector or Matrix
(a) Indirect control structure
Plant
(HEPHAISTOS)
NN Controller
Target
Temperature
Yt Measured
Temperature
Yr
Control
Input
U
(b)Direct control structure
Figure4.4. Twocontrol structuresofNNcontroller.
Theweightsof thisNNcontroller isusually tunedusingtheunsuper-
vised learning algorithms. Unlike in supervised learning, where the
targetoutputof theNNisgiveninadvance, inunsupervisedlearning
there is no explicit target output provided. Therefore the main chal-
lenge in using a direct NN controller is to determine a suitable and
realistic learning algorithm that can effectively tune the weights and
guarantee thecontrolperformance.
Thedynamicsoftherealplanttobecontrolledcanbedescribedbythe
expression, suchas
Y(k+1) =F (Y(k),U(k)), (4.20)
102
back to the
book Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources"
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
- Category
- Technik