- Tytuł:
- Recurrent neural networks for dynamic reliability analysis
- Autorzy:
-
Cadini, F.
Zio, E.
Pedroni, N. - Powiązania:
- https://bibliotekanauki.pl/articles/2069583.pdf
- Data publikacji:
- 2007
- Wydawca:
- Uniwersytet Morski w Gdyni. Polskie Towarzystwo Bezpieczeństwa i Niezawodności
- Tematy:
-
dynamic reliability analysis
infinite impulse response
locally recurrent neural network
long-term non-linear dynamics
system state memory
simplified nuclear reactor - Opis:
- A dynamic approach to the reliability analysis of realistic systems is likely to increase the computational burden, due to the need of integrating the dynamics with the system stochastic evolution. Hence, fast-running models of process evolution are sought. In this respect, empirical modelling is becoming a popular approach to system dynamics simulation since it allows identifying the underlying dynamic model by fitting system operational data through a procedure often referred to as ‘learning’. In this paper, a Locally Recurrent Neural Network (LRNN) trained according to a Recursive Back-Propagation (RBP) algorithm is investigated as an efficient tool for fast dynamic simulation. An application is performed with respect to the simulation of the non-linear dynamics of a nuclear reactor, as described by a simplified model of literature.
- Źródło:
-
Journal of Polish Safety and Reliability Association; 2007, 1; 45--53
2084-5316 - Pojawia się w:
- Journal of Polish Safety and Reliability Association
- Dostawca treści:
- Biblioteka Nauki
Artykuł