Abstract
This paper describes the application of Hybrid Intelligent Systems (HIS) in a new domain: the reliability of complex networks. The reliability of a network is assessed by employing two algorithms, TREPAN and Adaptive Neuro-Fuzzy Inference Systems ANFIS belonging to the HIS paradigm. TREPAN is a technique to extract linguistic rules from a trained Neural Network, and ANFIS is a method that combines fuzzy inference systems and neural networks. A numerical example, related to a complex network, illustrates the application of the approach and shows that HIS is a promising approach for reliability assessment. The structure function of the complex network analyzed is properly emulated by training both algorithms on a subset of possible system configurations, generated by a Monte Carlo simulation and an appropriate Evaluation Function. Both algorithms successfully describe the network status through a set of rules, which allows the reliability assessment.
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Torres D., D.E., Rocco S., C.M. (2005). Empirical Models Based on Hybrid Intelligent Systems for Assessing the Reliability of Complex Networks. In: Rothlauf, F., et al. Applications of Evolutionary Computing. EvoWorkshops 2005. Lecture Notes in Computer Science, vol 3449. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-32003-6_15
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DOI: https://doi.org/10.1007/978-3-540-32003-6_15
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