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Statistical-learning control of multiple-delay systems with application to ATM networks
Kybernetika, Tome 37 (2001) no. 3, pp. 355-365 Cet article a éte moissonné depuis la source Czech Digital Mathematics Library

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Congestion control in the ABR class of ATM network presents interesting challenges due to the presence of multiple uncertain delays. Recently, probabilistic methods and statistical learning theory have been shown to provide approximate solutions to challenging control problems. In this paper, using some recent results by the authors, an efficient statistical algorithm is used to design a robust, fixed-structure, controller for a high-speed communication network with multiple uncertain propagation delays.
Congestion control in the ABR class of ATM network presents interesting challenges due to the presence of multiple uncertain delays. Recently, probabilistic methods and statistical learning theory have been shown to provide approximate solutions to challenging control problems. In this paper, using some recent results by the authors, an efficient statistical algorithm is used to design a robust, fixed-structure, controller for a high-speed communication network with multiple uncertain propagation delays.
Classification : 68T05, 90B18, 93C23, 93C73, 93D21
Keywords: multiple uncertain delays; statistical learning theory 
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     title = {Statistical-learning control of multiple-delay systems with application to {ATM} networks},
     journal = {Kybernetika},
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     year = {2001},
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     zbl = {1265.93210},
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Abdallah, Chaouki T.; Ariola, Marco; Koltchinskii, Vladimir. Statistical-learning control of multiple-delay systems with application to ATM networks. Kybernetika, Tome 37 (2001) no. 3, pp. 355-365. http://geodesic.mathdoc.fr/item/KYB_2001_37_3_a10/

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