Blind restoration of nonlinearly mixed signals using multilayer polynomial neural network

IEE Proceedings: Vision, Image and Signal Processing

Volumn 151, Issue 1, 2004, Pages 51-61

  Woo, W L ^a   Khor, L C ^a  

^a NEWCASTLE UNIVERSITY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

GENETIC ALGORITHMS; INDEPENDENT COMPONENT ANALYSIS; MIXER CIRCUITS; NEURAL NETWORKS; NONLINEAR CONTROL SYSTEMS; POLYNOMIALS; RANDOM PROCESSES; RESTORATION;

NONLINEARLY MIXED SIGNALS; POLYNOMIAL NEURAL NETWORKS;

SIGNAL PROCESSING;

EID: 1542476946     PISSN: 1350245X     EISSN: None     Source Type: Journal    
DOI: 10.1049/ip-vis:20040302     Document Type: Conference Paper

References (18)

1. Amari, S., and Cichocki, A.: 'Adaptive blind signal processing: neural network approach', Proc. IEEE, 1998, 86, (10), pp. 2026-2048
2. Comon, P.: 'Independent component analysis: a new concept?', Signal Process., 1994, pp. 287-314
3. Hyvarinen, A., Karhunen, J., and Oja, E.: 'Independent component analysis' (John Wiley, 2001)
4. Belouchari, A., Abed-Meriam, K., Cardoso, J.F., and Moulines, E.: 'A blind source separation technique using second order statistics', IEEE Trans. Signal Process., 1997, pp. 434-443
5. Saleh, A.: 'Frequency-independent and frequency-dependent non-linear models of TWT amplifiers', IEEE Trans. Commun., 1981, COM-29, (11), pp. 121-129
6. Marmarelis, P.Z., and Marmarelis, V.Z.: 'Analysis of physiological systems' (Plenum Press, New York, 1978)
7. Burel, G.: 'BSS: a nonlinear neural algorithm', Neural Netw., 1992, 5, pp. 937-947
8. Yang, H.H., and Amari, S.: 'Information-theoretic approach to blind source separation of sources in non-linear mixture', Signal Process., 1998, 64, pp. 291-300
9. Taleb, A., and Jutten, C.: 'Source separation in post-nonlinear mixtures', IEEE Trans. Signal Process., 1999, 47, (10), pp. 2807-2820
10. Lee, T.W.: 'Blind source separation of nonlinear mixing models', Neural Netw., 1997, pp. 121-131
11. Pajunen, P., Hyvarinen, A., and Karhunen, J.: 'Nonlinear blind source separation by self-organising maps', in 'Progress in neural information processing' (MIT Press, 1996), pp. 1207-1210
12. Tan, Y., Wang, J., and Zurada, J.: 'Nonlinear blind source separation using a radial basis function network', IEEE Trans. Neural Netw., 2001, 12, (1), pp. 124-134
13. Valpora, H., Honkela, A., and Karhunen, J.: 'Nonlinear static and dynamic blind source separation using ensemble learning'. Proc. Int. Joint Conf. on Neural networks, Washington DC, USA, July 2001
14. Woo, W.L., and Sali, S.: 'General multilayer perceptron demixer scheme for nonlinear blind signal separation', IEE Proc., Vis. Image Signal Process., 2002, 149, (5), pp. 253-262
15. Kullback, S.: 'Information theory and statistics' (John Wiley & Sons, 1959)
16. Cantrell, C.D.: 'Modern mathematical methods for physicists and engineers' (Cambridge University Press, 2000)
17. Holland, J.H.: 'Adaptation in natural and artificial systems' (University of Michigan Press, 1975)
18. Ng, S.C., Chung, C.Y., Leung, S.H., and Luk, A.: 'An evolutionary search algorithm for adaptive IIR equaliser'. Proc. Int. Symp. on Circuits and systems, London, UK, 1994, pp. 53-56