FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3032821910> ?p ?o ?g. }

• W3032821910 endingPage "2348" @default.
• W3032821910 startingPage "2336" @default.
• W3032821910 abstract "Fuzzy cognitive maps (FCMs) have been successfully applied to time series forecasting. However, it still remains challenging to handle multivariate long nonstationary time series, such as EEG data, which may change rapidly and have patterns of trend. To overcome this limitation, in this article, we propose a fast prediction model to deal with multivariate long nonstationary time series based on the combination of elastic net and high order fuzzy cognitive map (HFCM), which is termed as ElasticNet <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>HFCM</sub> . The designed FCM models each variable by one node and the high-order FCM helps to capture the patterns of trend. A case study on predicting human actions through the Electroencephalogram (EEG) data in the form of multichannel long nonstationary time series is investigated based on the proposed prediction model. Specifically, we first predict EEG signals based on the historical data, then a 1D-convolutionary neural network (1D-CNN) is developed to classify the predicted time series. The experimental results on the Grasp-and-Lift dataset show that the proposal can predict the EEG data with lower prediction error compared with the other regression methods. The area under the curve scores obtained on the Grasp-and-Lift dataset by 1D-CNN are higher than those obtained by state-of-the-art classification methods for EEG data in most cases. These results illustrate that the proposal can predict and classify multivariate long nonstationary time series with high accuracy and efficiency." @default.
• W3032821910 created "2020-06-05" @default.
• W3032821910 creator A5011477215 @default.
• W3032821910 creator A5035282947 @default.
• W3032821910 creator A5037706636 @default.
• W3032821910 date "2021-08-01" @default.
• W3032821910 modified "2023-10-05" @default.
• W3032821910 title "Multivariate Time Series Forecasting Based on Elastic Net and High-Order Fuzzy Cognitive Maps: A Case Study on Human Action Prediction Through EEG Signals" @default.
• W3032821910 cites W1504199162 @default.
• W3032821910 cites W1566314748 @default.
• W3032821910 cites W1902052229 @default.
• W3032821910 cites W1999994061 @default.
• W3032821910 cites W2024826298 @default.
• W3032821910 cites W2030396580 @default.
• W3032821910 cites W2047755165 @default.
• W3032821910 cites W2048281837 @default.
• W3032821910 cites W2060672078 @default.
• W3032821910 cites W2066031679 @default.
• W3032821910 cites W2079786247 @default.
• W3032821910 cites W2089085970 @default.
• W3032821910 cites W2091630872 @default.
• W3032821910 cites W2095224843 @default.
• W3032821910 cites W2096451472 @default.
• W3032821910 cites W2097644370 @default.
• W3032821910 cites W2119438891 @default.
• W3032821910 cites W2121657501 @default.
• W3032821910 cites W2122825543 @default.
• W3032821910 cites W2135046866 @default.
• W3032821910 cites W2137947700 @default.
• W3032821910 cites W2141014056 @default.
• W3032821910 cites W2148605346 @default.
• W3032821910 cites W2153733695 @default.
• W3032821910 cites W2158698691 @default.
• W3032821910 cites W2159803222 @default.
• W3032821910 cites W2312717662 @default.
• W3032821910 cites W2344135745 @default.
• W3032821910 cites W2520865593 @default.
• W3032821910 cites W2523627082 @default.
• W3032821910 cites W2558953417 @default.
• W3032821910 cites W2559820885 @default.
• W3032821910 cites W2587401669 @default.
• W3032821910 cites W2591602428 @default.
• W3032821910 cites W2675260466 @default.
• W3032821910 cites W2738653582 @default.
• W3032821910 cites W2794833149 @default.
• W3032821910 cites W2802161886 @default.
• W3032821910 cites W947822678 @default.
• W3032821910 cites W2809442112 @default.
• W3032821910 doi "https://doi.org/10.1109/tfuzz.2020.2998513" @default.
• W3032821910 hasPublicationYear "2021" @default.
• W3032821910 type Work @default.
• W3032821910 sameAs 3032821910 @default.
• W3032821910 citedByCount "15" @default.
• W3032821910 countsByYear W30328219102020 @default.
• W3032821910 countsByYear W30328219102021 @default.
• W3032821910 countsByYear W30328219102022 @default.
• W3032821910 countsByYear W30328219102023 @default.
• W3032821910 crossrefType "journal-article" @default.
• W3032821910 hasAuthorship W3032821910A5011477215 @default.
• W3032821910 hasAuthorship W3032821910A5035282947 @default.
• W3032821910 hasAuthorship W3032821910A5037706636 @default.
• W3032821910 hasConcept C118552586 @default.
• W3032821910 hasConcept C119857082 @default.
• W3032821910 hasConcept C124101348 @default.
• W3032821910 hasConcept C139002025 @default.
• W3032821910 hasConcept C143724316 @default.
• W3032821910 hasConcept C151406439 @default.
• W3032821910 hasConcept C151730666 @default.
• W3032821910 hasConcept C153180895 @default.
• W3032821910 hasConcept C154945302 @default.
• W3032821910 hasConcept C15744967 @default.
• W3032821910 hasConcept C161584116 @default.
• W3032821910 hasConcept C171268870 @default.
• W3032821910 hasConcept C195975749 @default.
• W3032821910 hasConcept C199360897 @default.
• W3032821910 hasConcept C29470771 @default.
• W3032821910 hasConcept C41008148 @default.
• W3032821910 hasConcept C5041914 @default.
• W3032821910 hasConcept C50644808 @default.
• W3032821910 hasConcept C522805319 @default.
• W3032821910 hasConcept C58166 @default.
• W3032821910 hasConcept C86803240 @default.
• W3032821910 hasConceptScore W3032821910C118552586 @default.
• W3032821910 hasConceptScore W3032821910C119857082 @default.
• W3032821910 hasConceptScore W3032821910C124101348 @default.
• W3032821910 hasConceptScore W3032821910C139002025 @default.
• W3032821910 hasConceptScore W3032821910C143724316 @default.
• W3032821910 hasConceptScore W3032821910C151406439 @default.
• W3032821910 hasConceptScore W3032821910C151730666 @default.
• W3032821910 hasConceptScore W3032821910C153180895 @default.
• W3032821910 hasConceptScore W3032821910C154945302 @default.
• W3032821910 hasConceptScore W3032821910C15744967 @default.
• W3032821910 hasConceptScore W3032821910C161584116 @default.
• W3032821910 hasConceptScore W3032821910C171268870 @default.
• W3032821910 hasConceptScore W3032821910C195975749 @default.
• W3032821910 hasConceptScore W3032821910C199360897 @default.
• W3032821910 hasConceptScore W3032821910C29470771 @default.
• W3032821910 hasConceptScore W3032821910C41008148 @default.

• next