A machine learning approach to estimate frequency, duration & availability indexes in complex networks

Frequency, duration and availability are key measures in the evaluation of complex networks. Although efficient techniques have been developed, the calculation of these indexes is, however very difficult in certain type of networks, such as complex capacity-limited networks or in k-terminal problems. In this paper the machine learning algorithm Hamming clustering (HC), belonging to the family of rule generation methods, is employed to obtain an approximated availability expression (AE) for a network, under any success criterion. The AE can be used to evaluate the system availability and then could be transformed, using a set of specific rules, to evaluate system frequency. Two examples related to a complex network are evaluated using the proposed approach. The experiments show that the proposed method, using samples from a Monte Carlo simulation, yield excellent predictions for availability, frequency and duration indexes, with errors less than 1%.