Title

Application of neural networks to the adaptive routing control and traffic estimation of survivable wireless communication networks

Abstract

The problems of estimating and optimizing the behavior of wireless networks, based on the structure of a general stochastic model of the network's discrete-event dynamics, lead to mathematically correct, yet computationally intractable, backward recursive conditions defining the stochastic filter of network state and the optimal routing controls. The structure of the stochastic model of network dynamics, reflected in these recursive conditions, strongly parallels the recursive structure found in back-propagation neural networks. This structural resemblance has suggested the use of variations of the back-propagation approach to compute solutions to the recursive mathematical conditions. Because the structure of the network model is not, in general, feed-forward, three variations of recurrent back-propagation algorithms are proposed to solve a partitioned version of the defining filter and optimality conditions. Foreknowledge of the random characteristics of a given network model further suggest which back-propagation technique is appropriate to the application.

Publication Date

1-1-1994

Publication Title

Southcon Conference Record

Number of Pages

85-91

Document Type

Article; Proceedings Paper

Identifier

scopus

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/southc.1994.498080

Socpus ID

0028749360 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/0028749360

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