Performance modeling of flame-assisted fuel cells based on a swirl burner

AIP Advances, Год журнала: 2024, Номер 14(1)

Опубликована: Янв. 1, 2024

Aiming at the problems of a narrow operating range and complex modeling Flame-assisted Fuel Cells (FFCs), an FFC system based on swirl burner is proposed, neural network algorithms are used to construct prediction model for polarization curve system. First, output voltage power values measured under different working conditions, various experimental parameters collected form dataset; second, correlation analysis method screen out that highly correlated with as input variables network; finally, constructed, back propagation (BP), long short term memory, 1D-CNN chosen examine applicability networks The characteristic results show can obtain maximum 10.6 V 7.71 W. average relative errors three 5.23%, 4.08%, 6.19%, respectively, BP algorithm showing best generalization ability. study provides support application in aerospace other fields.

Язык: Английский

Enhancing Financial Market Prediction with Reinforcement Learning and Ensemble Learning

IFIP advances in information and communication technology, Год журнала: 2024, Номер unknown, С. 32 - 46

Опубликована: Янв. 1, 2024

Язык: Английский

An intelligent framework based on optimized variational mode decomposition and temporal convolutional network: Applications to stock index multi-step forecasting

Expert Systems with Applications, Год журнала: 2024, Номер unknown, С. 126222 - 126222

Опубликована: Дек. 1, 2024

Язык: Английский