The neural network representation facilitates the estimation of reliability by Monte Carlo simulation, and the reliability achieved in each performance level, for a specific combination of the design parameters, is itself represented with a neural network.
This paper presents an overview on the simulation, modeling, and reliability of AlGaAs/GaAs HBTs.
By comparing results with traditional Monte Carlo simulation and fuzzy reliability model, two numerical examples evidence the feasibility and superiority of proposed method at fuzzy reliability analysis in practical engineering.
Monte-Carlo simulation and structural reliability methods are used to stochastically model spatially varying pressure coefficients, roof component failure, and load re-distribution across the roof.
Five baseline structures considering different roof shapes, geographic locations, nail types, and enclosure conditions were investigated using simulation and system reliability concepts.
After determining the HESS capacity and combination, in the light of the Monte Carlo simulation and the reliability evaluation method proposed in [11], the regulation and control ability of the HESS could be assessed when taking the uncertainties of the distributed energy system into consideration in both modes.
The simplest and most recent model by Shklovskii (2005) does not even require a computer simulation and uses reliability theory (Gavrilov and Gavrilova, 2001) for the immune system.
Solutions based on Bayes' theorem, Markov chain Monte Carlo simulations, and inverse reliability analysis are developed.
The main purpose of this paper is to propose a methodology for reliability assessment and updating of notched timber components based on mechanical (A-integral formulation) and reliability (simulation and Bayesian networks) methods, and experimental data.
