A self-organizing deep network architecture designed based on LSTM network via elitism-driven roulette-wheel selection for time-series forecasting

Basit Öğe Kaydı
dc.authoridzhou, kun/0000-0002-3858-2072
dc.contributor.authorZhou, Kun
dc.contributor.authorOh, Sung-Kwun
dc.contributor.authorPedrycz, Witold
dc.contributor.authorQiu, Jianlong
dc.contributor.authorSeo, Kisung
dc.date.accessioned2024-05-19T14:41:45Z
dc.date.available2024-05-19T14:41:45Z
dc.date.issued2024
dc.departmentİstinye Üniversitesien_US
dc.description.abstractIn this study, we propose a new self-organizing deep network architecture of fuzzy polynomial neural networks (FPNN) based on Fuzzy rule-based Polynomial Neurons (FPNs) and a long short-term memory (LSTM) network to solve the task of time-series forecasting. In the existing regression model based on polynomial neural networks (PNN), it is difficult to achieve high quality performance when predicting time series data, because this model lacks the ability to extract temporal and spatial information. Therefore, we propose a new architecture consisting of one LSTM (temporal) layer and several fuzzy polynomial (spatial) layers to overcome the above-mentioned shortcomings of PNN and enhance its predictive ability to approximate the data. The temporal layer consists of LSTM neurons that have inherently strong modeling capabilities to learn sequential information. The spatial layers are composed of Rule-based Polynomial Neurons (FPNs) that can effectively reflect the complex nonlinear structure found in the input space and granulate it using of the Fuzzy C-Means (FCM) clustering method. An elitism-driven roulette-wheel selection (E_RWS) is used to select appropriate neurons. E_RWS not only ensures that the neuron with the strongest fitting ability is selected but also increases the diversity of candidate neurons. According to the experimental results, the proposed model has a high prediction performance and outperforms many state-of-the-art prediction methods when applied to the real-world time-series.en_US
dc.description.sponsorshipNational Research Foundation of Korea (NRF) - Korea Government (MSIT) [61877033]; National Natural Science Foundation of China [ZR2019MF021, NRF-2021R1F1A1056102]; Natural Science Foundation of Shandong Province [RS-2023-00244355]; [NRF-2023K2A9A2A06060385]; [61833005]en_US
dc.description.sponsorshipThis work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea Government (MSIT) (NRF-2021R1F1A1056102, NRF-2023K2A9A2A06060385, and RS-2023-00244355), and also by the National Natural Science Foundation of China under Grant No. 61877033, 61833005, and also by Natural Science Foundation of Shandong Province under Grant No. ZR2019MF021.en_US
dc.identifier.doi10.1016/j.knosys.2024.111481
dc.identifier.issn0950-7051
dc.identifier.issn1872-7409
dc.identifier.scopus2-s2.0-85186757078en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org10.1016/j.knosys.2024.111481
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5155
dc.identifier.volume289en_US
dc.identifier.wosWOS:001203075000001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofKnowledge-Based Systemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectFuzzy Polynomial Neural Networks (Fpnn)en_US
dc.subjectLong Short -Term Memory Network (Lstm)en_US
dc.subjectFuzzy C-Means (Fcm) Clusteringen_US
dc.subjectElitism -Driven Roulette-Wheel Selectionen_US
dc.subject(E_Rws)en_US
dc.subjectTime -Series Forecastingen_US
dc.titleA self-organizing deep network architecture designed based on LSTM network via elitism-driven roulette-wheel selection for time-series forecastingen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu