Abstract
The reliability of communication networks is assessed by employing two machine learning algorithms, Support Vector Machines (SVM) and Hamming Clustering (HC), acting on a subset of possible system configurations, generated by a Monte Carlo simulation and an appropriate Evaluation Function. The experiments performed with two different reliability measures show that both methods yield excellent predictions, though the performances of models generated by HC are significantly better than those of SVM.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Mitchell, M., Peças Lopes, J. A., Fidalgo, J. N., and McCalley, J. (2000). Using a neural network to predict the dynamic frequency response of a power system to an under-frequency load shedding scenario. In IEEE Summer Meeting.
Muselli, Marco and Liberati, Diego (2002). Binary rule generation via Hamming Clustering. IEEE Transactions on Knowledge and Data Engineering, 14:1258–1268.
Reingold, E., Nievergelt, J., and Deo, N. (1977). Combinatorial Algorithms: Theory and Practice. New Jersey: Prentice Hall.
Rocco, C. M. (2003). A rule induction approach to improve Monte Carlo system reliability assessment. Reliability Engineering and System Safety, 82:87–94.
Rocco, C. M. and Moreno, J. M. (2002). Reliability evaluation using Monte Carlo simulation and support vector machine. In Lecture Notes in Computer Science, volume 2329, pages 147–155. Berlin: Springer-Verlag.
Rocco, C. M. and Muselli, M. (2004). Empirical models based on machine learning techniques for determining approximate reliability expressions. Reliability Engineering and System Safety, 83:301–309.
Stivaros, C. and Sutner, K. (1997). Optimal link assignments for all-terminal network reliabilit. Discrete Applied Mathematics, 75:285–295.
Vapnik, Vladimir N. (1998). Statistical Learning Theory. New York: John Wiley & Sons.
Yoo, Y. B. and Deo, N. (1988). A comparison of algorithms for terminal-pair reliability. IEEE Transactions on Reliability, 37:216–221.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer
About this paper
Cite this paper
Claudio, M., Rocco, S., Muselli, M. (2005). Assessing the Reliability of Communication Networks Through Maghine Learning Techniques. In: Apolloni, B., Marinaro, M., Tagliaferri, R. (eds) Biological and Artificial Intelligence Environments. Springer, Dordrecht. https://doi.org/10.1007/1-4020-3432-6_44
Download citation
DOI: https://doi.org/10.1007/1-4020-3432-6_44
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-3431-2
Online ISBN: 978-1-4020-3432-9
eBook Packages: Computer ScienceComputer Science (R0)