Evaluate voting system reliability using the Monte Carlo simulation and artificial neural network

The 2nd International Conference on Wireless Broadband and Ultra Wideband Communications, AusWireless 2007

Volumn , Issue , 2007, Pages 45-49

  Yeh, Wei Chang ^a   Yu, Chia Yen ^a   Lin, Chien Hsing ^a  

^a NATIONAL TSING HUA UNIVERSITY   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

DECISION MAKING; FAULT TOLERANT COMPUTER SYSTEMS; MONTE CARLO METHODS; NEURAL NETWORKS; RELIABILITY; SIGNAL RECEIVERS; ULTRA-WIDEBAND (UWB);

BOXBEHNKEN DESIGN (BBD); DISTRIBUTED SYSTEMS; FAULT TOLERANT SYSTEMS; K OUT-OF-N SYSTEMS; ORGANIZATION SYSTEM; REPLICATED DATABASE; RESPONSE SURFACE METHODOLOGY; TECHNICAL DECISION MAKING;

DISTRIBUTED DATABASE SYSTEMS;

EID: 47749087122     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/AUSWIRELESS.2007.32     Document Type: Conference Paper

References (5)

1. W.C. Yeh, "A MCS-RSM approach for network reliability to minimize the total cost", International Journal of Advanced Manufacturing Technology, 22, 2003, pp. 681-688.
2. G. Levitin, A. Lisnianski, "Reliability optimization for weighted voting system", Reliability Engineering and System Safety, 71, 2001, pp. 131-138.
3. Fausett, L., Fundamentals of neural networks: architectures, algorithms, and applications, Prentice-Hall, Inc. Upper Saddle River, NJ, USA, 1994.
4. D. C. Montgomery, Design and Analysis of Experiments, Wiley, New York, USA, 2005.
5. Myers, R.H., Montgomery D.C., Response Surface Methodology-process and product optimization using designed, Wiley, New York, USA, 1995.