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5.2. ResultsofSystemIdentification
0 2 0 0 4 0 0 6 0 0 8 0 0 1 0 0
00
5
1 0
1 5
2 0
T i m e ( s )
E K
FB
P
MS
W B P
(b)Comparisonof thestandarddeviation.
Figure5.16. Validationresultsof threealgorithms.
Algorithm LearningMSE MeanSDof
validation Time(per
update)
EKF 0.1172 0.7743 0.0031s
BPM 0.0990 9.8806 0.0015s
SWBP 0.0974 10.4665 0.3446s
Table5.2. Performancecomparisonof threeonlineNNtrainingalgorithms.
to the highest estimation accuracy and more comprehensive perfor-
mance, the EKF algorithm is applied in the NNC system to train the
systemestimator.
Performance of both grey-box and black-box approaches can be com-
pared qualitatively according to above results. On the one hand, both
linearandnonlinearon-linerecursivesystemidentificationalgorithms
havefastconvergingspeedthantheNNapproaches. Butontheother
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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
- Category
- Technik