In this paper, Neural Network (NN) models for the real-time simulation of gas turbines are studied and developed. The analyses carried out are aimed at the selection of the most appropriate NN structure for gas turbine simulation, in terms of both computational time of the NN training phase and accuracy and robustness with respect to measurement uncertainty. In particular, feed-forward NNs, with a single hidden layer and different numbers of neurons, trained by using a back-propagation learning algorithm are considered and tested. Finally, a general procedure for the validation of computational codes is adapted and applied to the validation of the developed NN models.
Set Up of a Robust Neural Network for Gas Turbine Simulation / BETTOCCHI R.; PINELLI M.; SPINA P.; VENTURINI M.; BURGIO M.. - STAMPA. - 4:(2004), pp. 543-551. (Intervento presentato al convegno 2004 ASME Turbo Expo tenutosi a Vienna; Austria nel 4 June 2004-17 June 2004).
Set Up of a Robust Neural Network for Gas Turbine Simulation
SPINA, PIER RUGGERO;
2004
Abstract
In this paper, Neural Network (NN) models for the real-time simulation of gas turbines are studied and developed. The analyses carried out are aimed at the selection of the most appropriate NN structure for gas turbine simulation, in terms of both computational time of the NN training phase and accuracy and robustness with respect to measurement uncertainty. In particular, feed-forward NNs, with a single hidden layer and different numbers of neurons, trained by using a back-propagation learning algorithm are considered and tested. Finally, a general procedure for the validation of computational codes is adapted and applied to the validation of the developed NN models.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
