Nonlinear Blind Source Deconvolution Using Recurrent Prediction-Error Filters and an Artificial Immune System

Authors:
Cristina Wada;Douglas M. Consolaro;Rafael Ferrari;Ricardo Suyama;Romis Attux;Fernando J. Zuben
Affiliations:
Department of Computer Engineering and Industrial Automation (DCA),;Department of Computer Engineering and Industrial Automation (DCA),;Department of Microwave and Optics (DMO) School of Electrical and Computer Engineering, University of Campinas (UNICAMP), Campinas, Brazil CEP 13083-970;Department of Microwave and Optics (DMO) School of Electrical and Computer Engineering, University of Campinas (UNICAMP), Campinas, Brazil CEP 13083-970;Department of Computer Engineering and Industrial Automation (DCA),;Department of Computer Engineering and Industrial Automation (DCA),
Venue:
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Year:
2009

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Abstract

In this work, we propose a general framework for nonlinear prediction-based blind source deconvolution that employs recurrent structures (multi-layer perceptrons and an echo state network) and an immune-inspired optimization tool. The paradigm is tested under different channel models and, in all cases, the presence of feedback loops is shown to be a relevant factor in terms of performance. These results open interesting perspectives for dealing with classical problems such as equalization and blind source separation.