GREY NEURAL-NETWORK

This paper is to propose a Markov reliability model which includes the effects of permanent fault, intermittent fault, and transient fault f or reliability evaluations. We also provide a new neural network and a n improved training algorithm to evaluate the reliability of the fault -tolerant systems. The simulation results show that the neuro-based re liability model can converge faster than that of the other methods. Th e system state equations for the Markov model are a set of first-order linear differential equations. Usually the system reliability can be evaluated from the combined state solutions. This technique is very co mplicated and very difficult in the complex fault-tolerant systems. In this paper, we present a Grey Models (GM(1,1), DF-GM(1, 1) and ERC-GM (1, 1)) to evaluate the reliability of computer system. It can obtain the system reliability more directly and simply than the Markov model. But the data number for grey model that gets minimal error is differe nt in each time step. Therefore, a feedforward neural network is desig ned on the basis of more accurate prediction for the grey modeling to evaluate the reliability Finally, the simulation results show that thi s technique can lead to better accuracy than the Grey Model.