Desconvolução não supervisionada por filtros de erro de predição não lineares e recorrentes e sistemas imunologicos artificiais / Unsupervised deconvolution by nonlinear recurrent prediction-error filters and artificial immune systems

AUTOR(ES)
DATA DE PUBLICAÇÃO

2010

RESUMO

When data is transmitted trough a channel, it may be subject to several sorts of distortion that might cause unacceptable level of degradation. A very usual type of distortion is the intersymbol interference ,which is a consequence of the temporal spread of the information-bearing signal .To mitigate this interference ,it is usual to employ an equalizer ,which can be adapted either in a supervised or an unsupervised manner. For the latter case, a predictive structure, optimized according to the mean squared error criterion, is a classical solution. In the linear context, it is known that this approach is efficient only for minimum- or maximum-phase channels: to deal with mixed-phase channels, it is necessary to resort to nonlinear structures. In this work, we investigate the relevance, in this context, of the use of nonlinear predictors with feedback loops. The performance of nonlinear neural structures is analyzed in asset of representative channels, in order to form a better understanding of the effect of the channel memory on the signal and to make use of it in the deconvolution process. An optimization algorithm based on the concept of artificial immune systems is applied in the adaptation of predictors, due to its powerful global search capabilities and robustness to unstable solutions

ASSUNTO(S)

sistemas não-lineares feedback signal processing evolutive computation processamento de sinais nonlinear systems realimentação artificial neural networks redes neurais artificiais computação evolutiva

ACESSO AO ARTIGO

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