FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3048891995> ?p ?o ?g. }

• W3048891995 endingPage "27704" @default.
• W3048891995 startingPage "27694" @default.
• W3048891995 abstract "The novelty of this paper is to suggest an effective method according to the application of the Rotor Hopfield Neural Network optimized by the Grey Wolf Optimization (GWO) method for the identification of the Solid Oxide Fuel Cell (SOFC) model. In this literature, the basic required metrics to present the transient models of Solid Oxide Fuel Cell are defined. The proposed model is a hybrid model that is composed of the Rotor Hopfield Neural Network (RHNN) optimized by the GWO algorithm. The hybrid RHNN-GWO model, including a steady-state RHNN Neural Network, ensured by an optimization method. The RHNN algorithm is presented to assess the metrics of the RHNN-GWO model. In contrast to the wavering, the Mean Squared Error (MSE) for the RHNN-GWO model is calculated by 0.0017. The presented model results are examined with some well-known model results. The lowest values for Mean Squared Error belongs to the RHNN-GWO model. Also, the proposed model conserves a tremendous value of calculation time compared to the other models. Also, the proposed model shows a good agreement with SOFC results with lower computational difficulty. For 5000 samples, the variation of the voltage is in the [320,360]V interval, which completely follows the reference voltage of the SOFC." @default.
• W3048891995 created "2020-08-18" @default.
• W3048891995 creator A5040306767 @default.
• W3048891995 creator A5045548810 @default.
• W3048891995 creator A5056492561 @default.
• W3048891995 date "2020-10-01" @default.
• W3048891995 modified "2023-10-16" @default.
• W3048891995 title "Model identification and strategy application for Solid Oxide Fuel Cell using Rotor Hopfield Neural Network based on a novel optimization method" @default.
• W3048891995 cites W1964307491 @default.
• W3048891995 cites W1968491277 @default.
• W3048891995 cites W1973926912 @default.
• W3048891995 cites W1978725510 @default.
• W3048891995 cites W1983444850 @default.
• W3048891995 cites W1996854820 @default.
• W3048891995 cites W2004571307 @default.
• W3048891995 cites W2008201435 @default.
• W3048891995 cites W2012846448 @default.
• W3048891995 cites W2015833252 @default.
• W3048891995 cites W2018982456 @default.
• W3048891995 cites W2022150296 @default.
• W3048891995 cites W2036721013 @default.
• W3048891995 cites W2037310813 @default.
• W3048891995 cites W2041771484 @default.
• W3048891995 cites W2056496970 @default.
• W3048891995 cites W2061438946 @default.
• W3048891995 cites W2061842855 @default.
• W3048891995 cites W2062952286 @default.
• W3048891995 cites W2066916805 @default.
• W3048891995 cites W2067080722 @default.
• W3048891995 cites W2070804523 @default.
• W3048891995 cites W2076921391 @default.
• W3048891995 cites W2083060736 @default.
• W3048891995 cites W2087257766 @default.
• W3048891995 cites W2089035403 @default.
• W3048891995 cites W2135405405 @default.
• W3048891995 cites W2156784130 @default.
• W3048891995 cites W2466416084 @default.
• W3048891995 cites W2469892102 @default.
• W3048891995 cites W2489707867 @default.
• W3048891995 cites W2505801949 @default.
• W3048891995 cites W2573696012 @default.
• W3048891995 cites W2595893819 @default.
• W3048891995 cites W2603141317 @default.
• W3048891995 cites W270603342 @default.
• W3048891995 cites W2736813901 @default.
• W3048891995 cites W2762726101 @default.
• W3048891995 cites W2775516215 @default.
• W3048891995 cites W2883383020 @default.
• W3048891995 cites W2883971393 @default.
• W3048891995 cites W2902533895 @default.
• W3048891995 cites W2905741724 @default.
• W3048891995 cites W2910713102 @default.
• W3048891995 cites W2910874380 @default.
• W3048891995 cites W2912969644 @default.
• W3048891995 cites W2968300949 @default.
• W3048891995 doi "https://doi.org/10.1016/j.ijhydene.2020.07.127" @default.
• W3048891995 hasPublicationYear "2020" @default.
• W3048891995 type Work @default.
• W3048891995 sameAs 3048891995 @default.
• W3048891995 citedByCount "23" @default.
• W3048891995 countsByYear W30488919952020 @default.
• W3048891995 countsByYear W30488919952021 @default.
• W3048891995 countsByYear W30488919952022 @default.
• W3048891995 countsByYear W30488919952023 @default.
• W3048891995 crossrefType "journal-article" @default.
• W3048891995 hasAuthorship W3048891995A5040306767 @default.
• W3048891995 hasAuthorship W3048891995A5045548810 @default.
• W3048891995 hasAuthorship W3048891995A5056492561 @default.
• W3048891995 hasConcept C105795698 @default.
• W3048891995 hasConcept C111919701 @default.
• W3048891995 hasConcept C11413529 @default.
• W3048891995 hasConcept C119599485 @default.
• W3048891995 hasConcept C127413603 @default.
• W3048891995 hasConcept C139945424 @default.
• W3048891995 hasConcept C147789679 @default.
• W3048891995 hasConcept C154945302 @default.
• W3048891995 hasConcept C165801399 @default.
• W3048891995 hasConcept C17281054 @default.
• W3048891995 hasConcept C17525397 @default.
• W3048891995 hasConcept C185592680 @default.
• W3048891995 hasConcept C186060115 @default.
• W3048891995 hasConcept C2775924081 @default.
• W3048891995 hasConcept C2778456004 @default.
• W3048891995 hasConcept C2780799671 @default.
• W3048891995 hasConcept C33923547 @default.
• W3048891995 hasConcept C41008148 @default.
• W3048891995 hasConcept C47446073 @default.
• W3048891995 hasConcept C50644808 @default.
• W3048891995 hasConcept C78519656 @default.
• W3048891995 hasConcept C85617194 @default.
• W3048891995 hasConcept C86803240 @default.
• W3048891995 hasConcept C89395315 @default.
• W3048891995 hasConceptScore W3048891995C105795698 @default.
• W3048891995 hasConceptScore W3048891995C111919701 @default.
• W3048891995 hasConceptScore W3048891995C11413529 @default.
• W3048891995 hasConceptScore W3048891995C119599485 @default.
• W3048891995 hasConceptScore W3048891995C127413603 @default.
• W3048891995 hasConceptScore W3048891995C139945424 @default.

• next