Abstract
A communication network with stochastic input flows is considered. The nodes which route the traffic are required: i) to react instantaneously to the variations of their incoming flows so as to minimize an aggregate cost function, ii) to compute or adapt their routing strategies on line on the basis of the measured values of the incoming flows and of some local information (e.g., the characteristics, possibly time-varying, of the links connecting each node with the upstream and the downstream neighbours). Due to the first requirement, the routing nodes must be considered as the cooperating members of a team organization. The second requirement calls for a computationally distributed algorithm. This fact and the intractability, under general conditions, of team functional optimization problems lead us to structure the routing nodes as multi-layer feed-forward neural networks, for which the stochastic input flows play the role of training patterns. The weights of the routing neural networks are then adjusted by means of an efficient algorithm based on back-propagation and stochastic approximation.
Keywords
- Destination Node
- Stochastic Approximation
- Back Propagation Neural Network
- Incoming Flow
- Team Organization
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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References
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© 1991 Springer Science+Business Media New York
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Aicardi, M., Davoli, F., Minciardi, R., Zoppoli, R. (1991). The Use of Neural Networks in the Solution of Dynamic Routing Problems. In: New Trends in Systems Theory. Progress in Systems and Control Theory, vol 7. Birkhäuser, Boston, MA. https://doi.org/10.1007/978-1-4612-0439-8_6
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DOI: https://doi.org/10.1007/978-1-4612-0439-8_6
Publisher Name: Birkhäuser, Boston, MA
Print ISBN: 978-1-4612-6760-7
Online ISBN: 978-1-4612-0439-8
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