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Prediction of Cascading Failures in Spatial Networks.

Shunkun Y, Jiaquan Z, Dan L - PLoS ONE (2016)

Bottom Line: Cascading overload failures are widely found in large-scale parallel systems and remain a major threat to system reliability; therefore, they are of great concern to maintainers and managers of different systems.Fortunately, overload failures that propagate through a network exhibit certain spatial-temporal correlations, which allows the study of a group of nodes that share common spatial and temporal characteristics.We simulated overload failure propagations in a weighted lattice network that start with a center attack and predicted the failure percentages of different groups of nodes that are separated by a given distance.

View Article: PubMed Central - PubMed

Affiliation: School of Reliability and Systems Engineering, Beihang University, Beijing, China.

ABSTRACT
Cascading overload failures are widely found in large-scale parallel systems and remain a major threat to system reliability; therefore, they are of great concern to maintainers and managers of different systems. Accurate cascading failure prediction can provide useful information to help control networks. However, for a large, gradually growing network with increasing complexity, it is often impractical to explore the behavior of a single node from the perspective of failure propagation. Fortunately, overload failures that propagate through a network exhibit certain spatial-temporal correlations, which allows the study of a group of nodes that share common spatial and temporal characteristics. Therefore, in this study, we seek to predict the failure rates of nodes in a given group using machine-learning methods. We simulated overload failure propagations in a weighted lattice network that start with a center attack and predicted the failure percentages of different groups of nodes that are separated by a given distance. The experimental results of a feedforward neural network (FNN), a recurrent neural network (RNN) and support vector regression (SVR) all show that these different models can accurately predict the similar behavior of nodes in a given group during cascading overload propagation.

No MeSH data available.


Related in: MedlinePlus

Real value vs. predicted value of different models without considering α, n = 10000.(a) FNN. (b) RNN. (c) SVR. (d) Average error of each model.
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pone.0153904.g008: Real value vs. predicted value of different models without considering α, n = 10000.(a) FNN. (b) RNN. (c) SVR. (d) Average error of each model.

Mentions: We then repeated the proposed experiment on a larger lattice network with a size of 100×100; these results are shown in Fig 8. The three methods still perform well because they each present a small average absolute error and a narrow standard deviation. The SVR model is shown to perform marginally worse than the other two neural networks in this study; however, on the network with a size of 50×50, the SVR model performs better than the other models. This may occur because the neural networks are more complex and perform better at solving problems with multidimensional inputs.


Prediction of Cascading Failures in Spatial Networks.

Shunkun Y, Jiaquan Z, Dan L - PLoS ONE (2016)

Real value vs. predicted value of different models without considering α, n = 10000.(a) FNN. (b) RNN. (c) SVR. (d) Average error of each model.
© Copyright Policy
Related In: Results  -  Collection

License
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getmorefigures.php?uid=PMC4836660&req=5

pone.0153904.g008: Real value vs. predicted value of different models without considering α, n = 10000.(a) FNN. (b) RNN. (c) SVR. (d) Average error of each model.
Mentions: We then repeated the proposed experiment on a larger lattice network with a size of 100×100; these results are shown in Fig 8. The three methods still perform well because they each present a small average absolute error and a narrow standard deviation. The SVR model is shown to perform marginally worse than the other two neural networks in this study; however, on the network with a size of 50×50, the SVR model performs better than the other models. This may occur because the neural networks are more complex and perform better at solving problems with multidimensional inputs.

Bottom Line: Cascading overload failures are widely found in large-scale parallel systems and remain a major threat to system reliability; therefore, they are of great concern to maintainers and managers of different systems.Fortunately, overload failures that propagate through a network exhibit certain spatial-temporal correlations, which allows the study of a group of nodes that share common spatial and temporal characteristics.We simulated overload failure propagations in a weighted lattice network that start with a center attack and predicted the failure percentages of different groups of nodes that are separated by a given distance.

View Article: PubMed Central - PubMed

Affiliation: School of Reliability and Systems Engineering, Beihang University, Beijing, China.

ABSTRACT
Cascading overload failures are widely found in large-scale parallel systems and remain a major threat to system reliability; therefore, they are of great concern to maintainers and managers of different systems. Accurate cascading failure prediction can provide useful information to help control networks. However, for a large, gradually growing network with increasing complexity, it is often impractical to explore the behavior of a single node from the perspective of failure propagation. Fortunately, overload failures that propagate through a network exhibit certain spatial-temporal correlations, which allows the study of a group of nodes that share common spatial and temporal characteristics. Therefore, in this study, we seek to predict the failure rates of nodes in a given group using machine-learning methods. We simulated overload failure propagations in a weighted lattice network that start with a center attack and predicted the failure percentages of different groups of nodes that are separated by a given distance. The experimental results of a feedforward neural network (FNN), a recurrent neural network (RNN) and support vector regression (SVR) all show that these different models can accurately predict the similar behavior of nodes in a given group during cascading overload propagation.

No MeSH data available.


Related in: MedlinePlus