Online detection of the nature of complex-valued signals

Proceedings of the 2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing, MLSP 2006

Volumn , Issue , 2006, Pages 173-178

  Vayanos, Phebe ^a   Goh, Su Lee ^a   Mandic, Danilo P ^a  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL COMPLEXITY; COMPUTER SIMULATION; LEARNING ALGORITHMS; ONLINE SYSTEMS; PARAMETER ESTIMATION;

NONLINEAR GRADIENT DESCENT ALGORITHMS; SIGNAL MODALITY; SYNTHETIC BENCHMARK SIGNAL;

SIGNAL ANALYSIS;

EID: 38949186415     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/MLSP.2006.275543     Document Type: Conference Paper

References (23)

1. S. Haykin, Adaptive Filter Theory, Prentice-Hall, third edition, 1996.
2. D. P. Mandic and J. A. Chambers, Recurrent Neural Networks for Prediction: Architectures, learning Algorithms and Stability, Wiley, 2001.
3. V. J. Mathews, "Adaptive polynomial filters," IEEE Signal Processing Magazine, vol. 8, no. 3, pp. 10-26, 1991.
4. J. H. Mathews and R. W. Howell, Complex Analysis for Mathematics and Engineering, Jones and Bartlett Pub. Inc., third edition, 1997.
5. T. Needham, Visual Complex Analysis, Oxford University Press, 1997.
6. H. Kantz and T. Schreiber, Nonlinear Time Series Analysis, Cambridge University Press, second edition, 2004.
7. T. Gautama, D. P. Mandic, and M. Van Hulle, "A novel method for determining the nature of time series," IEEE Transactions on Biomedical Engineering, vol. 51, no. 5, pp. 728-736, 2004.
8. T. Gautama, D. P. Mandic, and M. Van Hulle, "On the indications of nonlinear structures in brain electrical activity," Physical Review E, vol. 67, no. 4, pp. 046204-1-046204-5, 2003.
9. T. Gautama, D. P. Mandic, and M. M. Van Hulle, "The delay vector variance method for detecting determinism and nonlinearity in time series," Physica D, vol. 190, no. 3-4, pp. 167-176, 2004.
10. T. Kim and T. Adali, "Approximation by fully complex multilayer perceptrons," Neural Computation, vol. 15, no. 7, pp. 1641-1666, 2003.
11. T. Adali, T. Kim, and V. Calhoun, "Independent component analysis by complex nonlinearities," Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, 2004, vol. 5, pp. V 525-528, 2004.
12. T. Gautama, D. P. Mandic, and M. Van Hulle, "A non-parametric test for detecting the complex-valued nature of time series," International Journal of Knowledge-Based Intelligent Engineering Systems, vol. 8, no. 2, pp. 99-106, 2004.
13. H. Leung and S. Haykin, "The complex backpropagation algorithm," IEEE Transactions on Signal Processing, vol. 3, no. 9, pp. 2101-2104, 1991.
14. N. Benvenuto and F. Piazza, "On the complex backpropagation algorithm," IEEE Transactions on Signal Processing, vol. 40, no. 4, pp. 967-969, 1992.
15. G. Kechriotis and E. S. Manolakos, "Training fully recurrent neural networks with complex weights," IEEE Transactions on Circuits and Systems-II: Analogue and Digital Signal Processing, pp. 235-238, 1994.
16. S. L. Goh and D. P. Mandic, "Nonlinear adaptive prediction of complex valued non-stationary signals," IEEE Transactions on Signal Processing, vol. 53, no. 5, pp. 1827-1836, 2005.
17. S. L. Goh and D. P. Mandic, "A general complex valued RTRL algorithm for nonlinear adaptive filters," Neural Computation, vol. 16, no. 12, pp. 2699-2731, 2004.
18. K. Aihara, (Ed), Applied Chaos and Applicable Chaos, Tokyo: Science-Sha, 1994.
19. S. Haykin and J. Principe, "Making sense of a complex world: Using neural networks to dynamically model chaotic events such as sea clutter," IEEE Signal Processing Magazine, vol. 15, no. 3, pp. 66-81, 1998.
20. A. R. Figueras-Vidal, J. Arenas-Garcia, and A. H. Saved, "Steady state performance of convex combinations of adaptive filters," in Proceedings of the International Conference on Acoustis, Speech and Signal Processing (ICASSP'05), 2005, pp. 33-36.
21. B. Jelfs, P. Vayanos, M. Chen, S. L. Goh, C. Boukis, T. Gautama, T. Rutkowski, T. Kuh, and D. P. Mandic, "An online method for detecting nonlinearity within a signal," in accepted for KES 2006, 2006, p. tba.
22. B. Widrow, J. McCool, and M. Ball, "The complex LMS algorithm," vol. 63, pp. 712-720, 1975.
23. D. P. Mandic, S. L. Goh, and A. I. Hanna, "Data reusing algorithms for complex valued adaptive filters," in Complex Valued Neural Networks, A. Hirose, Ed., pp. 131-153. World Scientific Publishing, Singapore, 2003.