Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Bayesian Assessment of Network Reliability
SIAM Review
Fusion of Neural Networks, Fuzzy Sets, and Genetic Algorithms: Industrial Applications
Fusion of Neural Networks, Fuzzy Sets, and Genetic Algorithms: Industrial Applications
Extracting comprehensible models from trained neural networks
Extracting comprehensible models from trained neural networks
Combinatorial Algorithms: Theory and Practice
Combinatorial Algorithms: Theory and Practice
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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.