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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Haykin, S. (ed.): Unsupervised Adaptive Filtering, vol. 2. Wiley, Chichester (2000)
Haykin, S.: Adaptive Filter Theory. Prentice Hall, Englewood Cliffs (1997)
Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. Wiley, Chichester (2001)
Cavalcante, C.C., Montalvão Filho, J.R., Dorizzi, B., Mota, J.C.M.: A Neural Predictor for Blind Equalization in Digital Communication. In: Proc. of AS-SPCC, Lake Louise, Canada (2000)
Ferrari, R., Suyama, R., Lopes, R.R., Attux, R.R.F., Romano, J.M.T.: An Optimal MMSE Fuzzy Predictor for SISO and MIMO Blind Equalization. In: Proceedings of the IAPR Workshop on Cognitive Information Processing (CIP), Santorini, Greece (2008)
Montalvão Filho, J., Dorizzi, B., Mota, J.C.M.: Some Theoretical Limits of Efficiency of Linear and Nonlinear Equalizers. Journal of Communication and Information Systems 14(2), 85–92 (1999)
Suyama, R., Duarte, L.T., Ferrari, R., Rangel, L.E.P., Attux, R.R.F., Cavalcante, C.C., Von Zuben, F.J., Romano, J.M.T.: A Nonlinear Prediction Approach to the Blind Separation of Convolutive Mixtures. EURASIP Journal on Applied Signal Processing 2007 (2007)
Haykin, S.: Neural Networks: A Comprehensive Foundation. MacMillan, Basingstoke (1994)
dos Santos, E.P., Von Zuben, F.J.: Efficient Second-Order Learning Algorithms for Discrete-Time Recurrent Networks. In: Recurrent Neural Networks: Design and Applications. CRC Press, Boca Raton (2000)
Castro, L.N.: Fundamentals of Natural Computing: Basic Concepts, Algorithms and Applications. Chapman & Hall, Boca Raton (2006)
Castro, L.N., Von Zuben, F.J.: Learning and Optimization Using the Clonal Selection Principle. IEEE Trans. on Evolutionary Computation 6(3), 239–251 (2002)
Proakis, J.G.: Digital Communications, 4th edn. McGraw-Hill, New York (2001)
Shynk, J.J.: Adaptive IIR filtering. ASSP Magazine, IEEE 6(2), 4–21 (1989)
Jaeger, H.: The Echo State Approach to Approach to Analyzing and Training Recurrent Neural Networks (Tech. Rep. n° 148). German National Research Center for Information Technology, Bremen (2001)
Ozturk, M.C., Xu, D., Principe, J.C.: Analysis and Design of Echo State Networks. Neural Computation 19, 111–138 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wada, C., Consolaro, D.M., Ferrari, R., Suyama, R., Attux, R., Von Zuben, F.J. (2009). Nonlinear Blind Source Deconvolution Using Recurrent Prediction-Error Filters and an Artificial Immune System. In: Adali, T., Jutten, C., Romano, J.M.T., Barros, A.K. (eds) Independent Component Analysis and Signal Separation. ICA 2009. Lecture Notes in Computer Science, vol 5441. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00599-2_47
Download citation
DOI: https://doi.org/10.1007/978-3-642-00599-2_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-00598-5
Online ISBN: 978-3-642-00599-2
eBook Packages: Computer ScienceComputer Science (R0)