Recurrent neural networks for dynamic reliability analysis

• Autorzy: Cadini F. , Zio E. , Pedroni N.
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

dynamic reliability analysis; infinite impulse response; locally recurrent neural network; long-term non-linear dynamics; system state memory; simplified nuclear reactor

• Wydawca: Polskie Towarzystwo Bezpieczeństwa i Niezawodności
• Czasopismo: Journal of Polish Safety and Reliability Association
• Rocznik: 2007
• Tom: Vol. 1
• Strony: 45--53
• Opis fizyczny: Bibliogr. 20 poz., rys., tab., wykr.

Twórcy

autor

Cadini F.

• Department of Nuclear Engineering, Polytechnic of Milan, Milan, Italy

autor

Zio E.

• Department of Nuclear Engineering, Polytechnic of Milan, Milan, Italy

autor

Pedroni N.

• Department of Nuclear Engineering, Polytechnic of Milan, Milan, Italy

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