General multilayer perceptron demixer scheme for nonlinear blind signal separation

IEE Proceedings: Vision, Image and Signal Processing

Volumn 149, Issue 5, 2002, Pages 253-262

  Woo, W L ^a   Sali, S ^a  

^a NEWCASTLE UNIVERSITY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

FEEDFORWARD NEURAL NETWORKS; LEARNING ALGORITHMS; MATHEMATICAL MODELS; MULTILAYER NEURAL NETWORKS; REAL TIME SYSTEMS;

MAXIMUM ENTROPY; MINIMUM MUTUAL INFORMATION; NONLINEAR DEMIXER MODEL;

BLIND SOURCE SEPARATION;

EID: 0036817390     PISSN: 1350245X     EISSN: None     Source Type: Journal    
DOI: 10.1049/ip-vis:20020548     Document Type: Article

References (20)

1. SALI, S.: 'Optimisation of satellite resources allocations'. European Union ACTS project report, no.ac.2002, December 1997
2. SALEH, A.: 'Frequency-independent and frequency-dependent non-linear models of TWT amplifiers', IEEE Trans. Commun., 1981, COM-29, (11), pp. 121-129
3. PONNEKANTI, S., and SALI, S.: 'Effective array schemes for mobile communications', Electron. Lett., 1999, 35, (1), pp. 21-23
4. GUA Y.J., VADGAMA, S., and TANAKA Y.: 'Advanced base station technologies for UTRAN', Electron. Commun. Eng. J., 2000, pp. 123-132
5. 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, 45, pp. 434-443
6. AMARI, S., and CICHOCKI, A.: 'Adaptive blind signal processing: neural network approach', Proc. IEEE, 1998, 86, (10)
7. AMARI, S.: 'Natural gradient works efficiently on learning', Neural Comput. 10, pp. 251-276
8. COMON, P., 'Independent component analysis: a new concept?', Signal Process., 1994, pp. 287-314
9. BELL, A.J., and SEJNOWSKI, T.J.: 'An information-maximisation to blind separation and blind deconvolution', Neural Comput., 1995, 7, pp. 1129-1159
10. HAYKIN, S.: 'Neural networks' (Prentice-Hall, New Jersey, 1999)
11. WOO, W.L., and SALI, S., 'A new scheme for blind source separation based on a general neural network model'. Proceedings of IEE 3G 2001 Conference, London, UK, 3-7 March 2001, pp. 22-28
12. 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
13. LEE, T.W.: 'Blind source separation of nonlinear mixing models', Neural Netw., 1997, 7, pp. 121-131
14. PAJUNEN, P., HYVARINEN, A., and KARHUNEN, J.: 'Nonlinear blind source separation by self-organising maps' in 'Progress in neural information processing (MIT Press, 1996), vol. 9, pp. 1207-1210
15. 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
16. TALEB, A., and JUTTEN, C.: 'Source separation in post-nonlinear mixtures', IEEE Trans. Signal Process., 1999, 47, (10), pp. 2807-2820
17. BUREL, G.: 'BSS: a nonlinear neural algorithm', Neural Netw., 1992, 5, pp. 937-947
18. VALPORA, H., HONKELA, A., and KARHUNEN, J.: 'Nonlinear static and dynamic blind source separation using ensemble learning'. Proceedings of Int. Joint Conference on Neural networks (IJCNN'01), Washington, DC, USA, July 2001
19. CYBENKO, G.: 'Approximation by superpositions of a sigmoidal function', Math. Control Signals Syst., 1997, 2, pp. 303-314
20. WOO, W.L., and SALI, S.: 'A new demixer scheme for blind source separation', Proceedings of IEEE ISSPA, Kuala Lumpur, Malaysia, 13-16 August 2001, pp. 128-132