Seite - 82 - in Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources
Bild der Seite - 82 -
Text der Seite - 82 -
3. ModelingMicrowaveHeating
trainingsetDas inclassicalbatch learning, thebasic idea is thatat
eachtimek, theformerNsdatapairs(slidingwindow)areusedfor
thetraining(fromtimek−Ns+1 to thecurrent timek). Therefore
thecost function ischangedinto
JSW(k) = 1
2Ns k∑
q=k−Ns+1 (
Y(q)−YNN(q)
)T(
Y(q)−YNN(q) )
.
(3.89)
Ateachtimek,notonlythecurrentobserveddatapairbutalsoNs
former data pairs are included in this cost function JSW(k). The
similar idea is widely used in neural network based time series
predictions, suchas in [FDH01].
These three algorithms are all fast and efficient online NN training
algorithms that are implemented in practical applications. They have
been tested and compared in real experiments of HEPHAISTOS, and
correspondingresultscanbefoundinchapter 5. Furtherdetailsabout
the neural network design and implementations refer to [HDB+96],
[Roj96], [Hay98]and[AN15].
82
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
- Kategorie
- Technik