IEICE TRANSACTIONS on Fundamentals
Grey Neural Network
Summary :
This paper is to propose a Markov reliability model which includes the effects of permanent fault, intermittent fault, and transient fault for reliability evaluations. We also provide a new neural network and an improved training algorithm to evaluate the reliability of the fault-tolerant systems. The simulation results show that the neuro-based reliability model can converge faster than that of the other methods. The 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 complicated 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 different in each time step. Therefore, a feedforward neural network is designed on the basis of more accurate prediction for the grey modeling to evaluate the reliability. Finally, the simulation results show that this technique can lead to better accuracy than the Grey Model.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E81-A No.11 pp.2433-2442
- Publication Date
- 1998/11/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- Category
- Neural Networks
Authors
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Chyun-Shin CHENG, Yen-Tseng HSU, Chwan-Chia WU, "Grey Neural Network" in IEICE TRANSACTIONS on Fundamentals,
vol. E81-A, no. 11, pp. 2433-2442, November 1998, doi: .
Abstract: This paper is to propose a Markov reliability model which includes the effects of permanent fault, intermittent fault, and transient fault for reliability evaluations. We also provide a new neural network and an improved training algorithm to evaluate the reliability of the fault-tolerant systems. The simulation results show that the neuro-based reliability model can converge faster than that of the other methods. The 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 complicated 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 different in each time step. Therefore, a feedforward neural network is designed on the basis of more accurate prediction for the grey modeling to evaluate the reliability. Finally, the simulation results show that this technique can lead to better accuracy than the Grey Model.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e81-a_11_2433/_p
Copy
@ARTICLE{e81-a_11_2433,
author={Chyun-Shin CHENG, Yen-Tseng HSU, Chwan-Chia WU, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Grey Neural Network},
year={1998},
volume={E81-A},
number={11},
pages={2433-2442},
abstract={This paper is to propose a Markov reliability model which includes the effects of permanent fault, intermittent fault, and transient fault for reliability evaluations. We also provide a new neural network and an improved training algorithm to evaluate the reliability of the fault-tolerant systems. The simulation results show that the neuro-based reliability model can converge faster than that of the other methods. The 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 complicated 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 different in each time step. Therefore, a feedforward neural network is designed on the basis of more accurate prediction for the grey modeling to evaluate the reliability. Finally, the simulation results show that this technique can lead to better accuracy than the Grey Model.},
keywords={},
doi={},
ISSN={},
month={November},}
Copy
TY - JOUR
TI - Grey Neural Network
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2433
EP - 2442
AU - Chyun-Shin CHENG
AU - Yen-Tseng HSU
AU - Chwan-Chia WU
PY - 1998
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E81-A
IS - 11
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - November 1998
AB - This paper is to propose a Markov reliability model which includes the effects of permanent fault, intermittent fault, and transient fault for reliability evaluations. We also provide a new neural network and an improved training algorithm to evaluate the reliability of the fault-tolerant systems. The simulation results show that the neuro-based reliability model can converge faster than that of the other methods. The 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 complicated 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 different in each time step. Therefore, a feedforward neural network is designed on the basis of more accurate prediction for the grey modeling to evaluate the reliability. Finally, the simulation results show that this technique can lead to better accuracy than the Grey Model.
ER -
English
