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    Artificial Neural Network Based Model for Calculating the Flow Composition Influence of Solid Oxide Fuel Cell

    Source: Journal of Fuel Cell Science and Technology:;2014:;volume( 011 ):;issue: 002::page 21001
    Author:
    Milewski, Jaros‚aw
    ,
    ڑwirski, Konrad
    DOI: 10.1115/1.4025922
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: The paper presents use of an artificial neural network (ANN) for predicting the thermalflow behavior of a solid oxide fuel cell with no algorithmic solution merely by utilizing available experimental data. The error backpropagation algorithm was used for an ANN training procedure.
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      Artificial Neural Network Based Model for Calculating the Flow Composition Influence of Solid Oxide Fuel Cell

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    http://yetl.yabesh.ir/yetl1/handle/yetl/155110
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    contributor authorMilewski, Jaros‚aw
    contributor authorڑwirski, Konrad
    date accessioned2017-05-09T01:08:59Z
    date available2017-05-09T01:08:59Z
    date issued2014
    identifier issn2381-6872
    identifier otherfc_011_02_021001.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/155110
    description abstractThe paper presents use of an artificial neural network (ANN) for predicting the thermalflow behavior of a solid oxide fuel cell with no algorithmic solution merely by utilizing available experimental data. The error backpropagation algorithm was used for an ANN training procedure.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleArtificial Neural Network Based Model for Calculating the Flow Composition Influence of Solid Oxide Fuel Cell
    typeJournal Paper
    journal volume11
    journal issue2
    journal titleJournal of Fuel Cell Science and Technology
    identifier doi10.1115/1.4025922
    journal fristpage21001
    journal lastpage21001
    identifier eissn2381-6910
    treeJournal of Fuel Cell Science and Technology:;2014:;volume( 011 ):;issue: 002
    contenttypeFulltext
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