The Random Neural Network Model for the On-Line Multicast Problem

Aiello, Giovanni; Gaglio, Salvatore; Re, Giuseppe Lo; Storniolo, Pietro; Urso, Alfonso

doi:10.1007/1-4020-3432-6_19

Cited by 2 publications

(1 citation statement)

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“…• RNNs have also been used to solve two problems close to MSTs: the dynamic multicast problem (Gelenbe et al, 1997;Aiello et al, 2005) and the access network design problem (Cancela et al, 2004).…”

Section: Other Applications Of the Rnn Modelmentioning

confidence: 99%

A Tutorial about Random Neural Networks in Supervised Learning

Basterrech,

Rubino

2016

Preprint

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Random Neural Networks (RNNs) are a class of Neural Networks (NNs) that can also be seen as a specific type of queuing network. They have been successfully used in several domains during the last 25 years, as queuing networks to analyze the performance of resource sharing in many engineering areas, as learning tools and in combinatorial optimization, where they are seen as neural systems, and also as models of neurological aspects of living beings. In this article we focus on their learning capabilities, and more specifically, we present a practical guide for using the RNN to solve supervised learning problems. We give a general description of these models using almost indistinctly the terminology of Queuing Theory and the neural one. We present the standard learning procedures used by RNNs, adapted from similar well-established improvements in the standard NN field. We describe in particular a set of learning algorithms covering techniques based on the use of first order and, then, of second order derivatives. We also discuss some issues related to these objects and present new perspectives about their use in supervised learning problems. The tutorial describes their most relevant applications, and also provides a large bibliography.

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Section: Other Applications Of the Rnn Modelmentioning

confidence: 99%