Neural networks for function approximation in dynamic modelling

• Autorzy: Nedbálek J.
• Języki publikacji: EN

Abstrakty

EN

The paper demonstrates the comparsion of Monte Carlo simulation (MC) algorithm with the Radial Basis Function (RBF) neural network enhancement of the same algorithm in the reliability case study. In our test, we dispose of the tank containing liquid water – its temperature process variable evolves deterministicly according to the differential equation, which solution is known. All component failures are considered as a stochastic events. In the case of surpassing temperature treshhold of the liquid inside the tank, we interpret the situation as the system failure. With regard to process dynamics, we attempt to evaluate the tank system unreliability related to the initiative input parameters setting. The neural network is used in equation coeficients calculation, which is executed in each transient state. Due to the neural networks, for some of the initial component settings, we can achieve the results of computation faster than in classical way of coeficients calculating and substituting into the equation.

Słowa kluczowe

EN

reliability; Monte Carlo; RBF neural network; simulation; temperature

• Wydawca: Polskie Towarzystwo Bezpieczeństwa i Niezawodności
• Czasopismo: Journal of Polish Safety and Reliability Association
• Rocznik: 2008
• Tom: Vol. 2
• Strony: 255--259
• Opis fizyczny: Bibliogr. 6 poz., rys., tab.

Twórcy

autor

Nedbálek J.

• VŠB – Technical University of Ostrava, Ostrava Poruba, Czech Republic

Bibliografia

• [1] Chen, S., Cowan, C. F. N. & Grant, P. M. (1991). Orthogonal Least Squares Learning Algorithm for Radial Basis Function Networks. IEEE Transactions on Neural Networks vol. 2, no. 2, March, 302-309.
• [2] Nedbálek, J. (2007). The Temperature Stability of Liquid in the Tank. Proc. Risk, Quality and Reliability, Ostrava 131-133.
• [3] Pasquet, S., Chatalet, E., Padovani, E. & Zio E. (1998). Use of Neural Networks to evaluate the RAMS` parameters of dynamic systems. Université de Technologie de Troyes France, Polytechnic of Milan Italy.
• [4] Pasquet, S., Chatalet, E.,Thomas, P. & Dutuit, Y. (1997). Analysis of a Sequential, Non- Coherent and Looped System with Two Approaches. Petri Nets and Neural Networks. Proc. of International conference on safety and reliability, ESREL’97, Lisabon, Portugal, 2257-2264.
• [5] Virius, M. (1985). Základy výpočetní techniky (Metoda Monte Carlo), ČVUT, Praha.
• [6] Yee, P. V. & Haykin, S. (2001). Regularized Radial Basis Function Networks: Theory and Applications. John Wiley.