1. Repositório Institucional UFC
2. CT - Centro de Tecnologia
3. DETE - Departamento de Engenharia de Teleinformática
4. DETE - Trabalhos apresentados em eventos

Tipo:	Artigo de Evento
Título:	On recurrent neural networks for auto-similar traffic prediction: a performance evaluation
Autor(es):	Menezes, José Wally Mendonça de Barreto, Guilherme de Alencar
Palavras-chave:	Recurrent neural networks;Traffic prediction;Auto-similar processes;VBR video traffic;Multi-step-ahead prediction
Data do documento:	2006
Instituição/Editor/Publicador:	International Telecommunications Symposium
Citação:	MENEZES, J. W. M.; BARRETO, G. A. On recurrent neural networks for auto-similar traffic prediction: a performance evaluation. In: INTERNATIONAL TELECOMMUNICATIONS SYMPOSIUM, 2006, Fortaleza. Anais... Fortaleza: IEEE, 2006. p. 534-539.
Resumo:	The NARX network is a recurrent neural architecture commonly used for input-output modelling of nonlinear systems. The input of the NARX network is formed by two tapped-delay lines, one sliding over the input signal and the other one over the output signal. Currently, when applied to nonlinear time series prediction, the NARX architecture is designed as a plain Focused Time Delay Neural Network (FTDNN); thus, limiting its predictive abilities. In this paper, we propose a strategy that allows the original NARX architecture to fully exploit its computational resources to improve prediction performance. We use real-world VBR video traffic time series to evaluate the proposed approach in multi-step-ahead prediction tasks. The results show that the proposed approach consistently outperforms standard neural network based predictors, such as the FTDNN and Elman architectures.
URI:	http://www.repositorio.ufc.br/handle/riufc/70695
Aparece nas coleções:	DETE - Trabalhos apresentados em eventos

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