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State Prediction of Chaotic Time-Series Systems Using Autoregressive Integrated with Adaptive Network-Fuzzy

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Abstract
In this research, advanced technology is used to monitoring chaotic
time-series signals. The combination of autoregressive with adaptive networkfuzzy
algorithms is suggested for chaotic signal prediction. The autoregressive
prediction algorithm is recommended for chaotic time-series prediction. This technique
is linear, and the modeling prediction accuracy has a limitation. To reduce
the root means square (RMS) error of prediction, the order of autoregressive prediction
should be increased which is caused to increase the number of parameters
and nonlinearity as well. Thus, the combination of autoregressive prediction with
an adaptive network-fuzzy algorithm is suggested to reduce the prediction error in
chaotic time-series signals. To test the power of the proposed prediction algorithm,
the 2nd order proposed method is compared with the 2nd order and 6th order of
ARtechnique, and the RMSerror in these three algorithms are 0.0967, 0.4953, and
0.3159, respectively. So far and compared to the classical autoregressive method,
the proposed prediction model is efficient for chaotic time-series signals.
Author(s)
필탄 파르진김종면
Issued Date
2021
Type
Article
Keyword
Adaptive network-fuzzy techniqueAutoregressive algorithmChaotic time-series signalsCondition monitoringState prediction
DOI
10.1007/978-3-030-85577-2_49
URI
https://oak.ulsan.ac.kr/handle/2021.oak/9082
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_springer_books_10_1007_978_3_030_85577_2_49&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,State%20Prediction%20of%20Chaotic%20Time-Series%20Systems%20Using%20Autoregressive%20Integrated%20with%20Adaptive%20Network-Fuzzy&offset=0&pcAvailability=true
Publisher
Lecture Notes in Networks and Systems
Location
스위스
Language
영어
ISSN
2367-3370
Citation Volume
308
Citation Number
1
Citation Start Page
415
Citation End Page
422
Appears in Collections:
Engineering > IT Convergence
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