Direct and inverse neural networks modelling applied to study the influence of the gas diffusion layer properties on PBI-based PEM fuel cells

International Journal of Hydrogen Energy

Volumn 35, Issue 15, 2010, Pages 7889-7897

  Lobato, Justo ^a   Cañizares, Pablo ^a   Rodrigo, Manuel A ^a   Piuleac, Ciprian George ^b   Curteanu, Silvia ^b   Linares, José J ^a  

^a UNIVERSITY OF CASTILLA LA MANCHA   (Spain)

^b GHEORGHE ASACHI TECHNICAL UNIVERSITY OF IASI   (Romania)

Author keywords

Direct and inverse neural modelling; GDL; Neuronal networks; PBI; PEMFC

Indexed keywords

ARTIFICIAL NEURAL NETWORK; CELL PERFORMANCE; CELL VOLTAGES; ELMAN NEURAL NETWORK; EXPERIMENTAL VALUES; FEED-FORWARD NETWORK; GAS DIFFUSION LAYERS; GENERALIZATION CAPABILITY; HIDDEN LAYERS; INITIAL CONDITIONS; INVERSE NEURAL NETWORK; MATHEMATICAL TOOLS; MEAN PORE SIZE; MODULAR NEURAL NETWORKS; MULTI LAYER PERCEPTRON; NEURONAL NETWORKS; PEM FUEL CELL; POLARIZATION CURVES; VALIDATION PHASE;

AIR PERMEABILITY; CURVE FITTING; DIFFUSION IN GASES; FUEL CELLS; MULTILAYER NEURAL NETWORKS;

FEEDFORWARD NEURAL NETWORKS;

EID: 77955511748     PISSN: 03603199     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijhydene.2010.05.065     Document Type: Article

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