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- W2993281570 abstract "Abstract The chapter presents applicative examples of artificial-neural networks (ANN) described in the contemporary literature and synthesizes the scientific and engineering information contained in a group of reviews assessing papers which apply artificial-neural networks. Various cases of ANN fuel cell modeling and pollutants modeling are considered. Some approaches apply the multilayer perceptron with unidirectional information flow. Hybrid models of ANN, with radial base function (RBF), are reviewed without going into detail regarding the network architecture and transition functions. The following topics are outlined: The role of ANN in the development of low pollution vehicles; NN hybrid model of direct internal reforming SOFC; NN-based dynamic control of PEM fuel cells; NN on-board FC power supply modeling; NN with SOFC control of power supply improvement; NN-based modeling of PEM FC and controller synthesis; NN model of drying and thermal degradation; NN modeling of SOFC; NN optimization of energy systems; NN model for fluidized bed; NN power control of a FC; performance prediction and analysis of the cathode catalyst layer; NN model for a PEM FC; hybrid NN models for fuel cells; ANN capillary transport characteristics of FC diffusion media; ANN of the mechanical behavior of PEM FC; ANN simulators for SOFC performance; ANN modeling the study and development of fuel cells; ANN to predict SOFC performance in residential spaces; NN modeling of polymer electrolyte membrane FC; GA-RBF neural networks modeling; and NN-based quality control with a SOFC plant. A review is given of central notions of the NN theory. A brief assessment of results obtained in these investigations is also provided. Finally, we direct the reader to a model of neural networks for emission prediction of dust pollutants." @default.
- W2993281570 created "2019-12-13" @default.
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- W2993281570 date "2020-01-01" @default.
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- W2993281570 title "Complex systems of neural networks" @default.
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- W2993281570 doi "https://doi.org/10.1016/b978-0-12-818594-0.00004-0" @default.
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