Title :
Fault modeling and reliability evaluations using artificial neural networks
Author :
Cheng, Chyun-Shin ; Hsu, Yen-Tseng ; Wu, Chwan-Chia
Author_Institution :
Dept. of Electr. Eng., Nat. Taiwan Univ., Taipei, Taiwan
Abstract :
In this paper, we present a generalized Markov reliability and fault-tolerant model (including the effects of permanent fault, transient fault and intermittent fault) for reliability evaluations based on the neural network techniques. The desired reliability of the system under design is fed to the neural network and when the neural network converges the parameters of the design are extracted from the weights of the neural network. We also obtained the simulation results which are in agreement with the classical analysis
Keywords :
Markov processes; failure analysis; fault tolerant computing; feedforward neural nets; reliability theory; Markov model; fault modeling; fault-tolerant model; feedforward neural networks; intermittent fault; permanent fault; reliability; transient fault; Analytical models; Artificial neural networks; Computational modeling; Computer network reliability; Fault tolerance; Feedforward neural networks; Feedforward systems; Neural networks; Neurons; Power system reliability;
Conference_Titel :
Neural Networks, 1995. Proceedings., IEEE International Conference on
Conference_Location :
Perth, WA
Print_ISBN :
0-7803-2768-3
DOI :
10.1109/ICNN.1995.488139