DocumentCode :
2978976
Title :
Simultaneous on-line monitoring and wave-net learning
Author :
Jafari, Masoumeh ; Safavi, Ali Akbar
Author_Institution :
Dept. of Power & Control Eng., Shiraz Univ., Shiraz, Iran
fYear :
2010
fDate :
11-13 May 2010
Firstpage :
686
Lastpage :
691
Abstract :
Current on-line wave-net learning algorithm adapts the primary identified process model with the new changes in time varying processes without a consideration of abnormal situations in the process operation. Therefore, if a disturbance occurs and makes changes in the process, current on-line learning updates the primary model to an unsuitable model. This paper proposes a procedure that first determines normal variations of time-varying processes from abnormal variations incorporating an adaptive dynamic principal component analysis (Adaptive DPCA) and updates the model only based on normal variations. A double continuously stirred tank reactors (CSTR) case study is invoked to show the effectiveness of the proposed approach. The results show the effectiveness of the method.
Keywords :
Computerized monitoring; Continuous-stirred tank reactor; Inductors; Multiresolution analysis; Neural networks; Power engineering and energy; Power engineering computing; Principal component analysis; Statistical analysis; Wavelet analysis; CSTR; DPCA; On-line learning; On-line monitoring; Wave-Nets; component;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electrical Engineering (ICEE), 2010 18th Iranian Conference on
Conference_Location :
Isfahan, Iran
Print_ISBN :
978-1-4244-6760-0
Type :
conf
DOI :
10.1109/IRANIANCEE.2010.5506984
Filename :
5506984
Link To Document :
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