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https://hdl.handle.net/1822/86985
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Campo DC | Valor | Idioma |
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dc.contributor.author | Mu Qiao | por |
dc.contributor.author | Yanchun Liang | por |
dc.contributor.author | Tavares, Adriano | por |
dc.contributor.author | Xiaohu Shi | por |
dc.date.accessioned | 2023-10-19T08:51:34Z | - |
dc.date.available | 2023-10-19T08:51:34Z | - |
dc.date.issued | 2023-06-24 | - |
dc.identifier.citation | Qiao, M.; Liang, Y.; Tavares, A.; Shi, X. Multilayer Perceptron Network Optimization for Chaotic Time Series Modeling. Entropy 2023, 25, 973. https://doi.org/10.3390/e25070973 | por |
dc.identifier.uri | https://hdl.handle.net/1822/86985 | - |
dc.description.abstract | Chaotic time series are widely present in practice, but due to their characteristics—such as internal randomness, nonlinearity, and long-term unpredictability—it is difficult to achieve high-precision intermediate or long-term predictions. Multi-layer perceptron (MLP) networks are an effective tool for chaotic time series modeling. Focusing on chaotic time series modeling, this paper presents a generalized degree of freedom approximation method of MLP. We then obtain its Akachi information criterion, which is designed as the loss function for training, hence developing an overall framework for chaotic time series analysis, including phase space reconstruction, model training, and model selection. To verify the effectiveness of the proposed method, it is applied to two artificial chaotic time series and two real-world chaotic time series. The numerical results show that the proposed optimized method is effective to obtain the best model from a group of candidates. Moreover, the optimized models perform very well in multi-step prediction tasks. | por |
dc.description.sponsorship | This research was funded in part by the NSFC grant numbers 61972174 and 62272192, the Science-Technology Development Plan Project of Jilin Province grant number 20210201080GX, the Jilin Province Development and Reform Commission grant number 2021C044-1, the Guangdong Universities’ Innovation Team grant number 2021KCXTD015, and Key Disciplines Projects grant number 2021ZDJS138. | por |
dc.language.iso | eng | por |
dc.publisher | Multidisciplinary Digital Publishing Institute (MDPI) | por |
dc.relation | 20210201080GX | por |
dc.relation | 2021C044-1 | por |
dc.relation | 2021KCXTD015 | por |
dc.relation | 2021ZDJS138 | por |
dc.rights | openAccess | por |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | por |
dc.subject | Chaotic time series | por |
dc.subject | Multilayer perceptron network | por |
dc.subject | Generalized degrees of freedom | por |
dc.subject | Akaike information criterion | por |
dc.subject | Maximal Lyapunov exponent | por |
dc.title | Multilayer perceptron network optimization for chaotic time series modeling | por |
dc.type | article | por |
dc.peerreviewed | yes | por |
dc.relation.publisherversion | https://www.mdpi.com/1099-4300/25/7/973 | por |
oaire.citationStartPage | 1 | por |
oaire.citationEndPage | 20 | por |
oaire.citationIssue | 7 | por |
oaire.citationVolume | 25 | por |
dc.date.updated | 2023-07-28T12:21:32Z | - |
dc.identifier.eissn | 1099-4300 | - |
dc.identifier.doi | 10.3390/e25070973 | por |
sdum.journal | Entropy | por |
oaire.version | VoR | por |
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Ficheiro | Descrição | Tamanho | Formato | |
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entropy-25-00973.pdf | 2,52 MB | Adobe PDF | Ver/Abrir |
Este trabalho está licenciado sob uma Licença Creative Commons