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Assessing the Reliability of Complex Networks through Hybrid Intelligent Systems

  • D.E. D. Torres
  • C.M. S. Rocco
Conference paper

Abstract

This paper describes the application of Hybrid Intelligent Systems in a new domain: reliability of complex networks. The reliability is assessed by employing two algorithms (TREPAN and Adaptive Neuro-Fuzzy Inference Systems (ANFIS)), both belonging to the Hybrid Intelligent Systems paradigm. TREPAN is a technique to extract linguistic rules from a trained Neural Network, whereas ANFIS is a method that combines fuzzy inference systems and neural networks. In the experiment presented, the structure function of the complex network analyzed is properly emulated by training both models on a subset of possible system configurations, generated by a Monte Carlo simulation and an appropriate Evaluation Function. Both approaches are able to successfully describe the network status through a set of rules, which allows the reliability assessment

Keywords

Fuzzy Model Fuzzy Inference System Fuzzy Neural Network ANFIS Model Hybrid Intelligent System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag/Wien 2005

Authors and Affiliations

  • D.E. D. Torres
    • 1
  • C.M. S. Rocco
    • 1
  1. 1.Facultad de IngenieríaUniversidad Central de VenezuelaVenezuela

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