A data-reusing nonlinear gradient descent algorithm for a class of complex-valued neural adaptive filters

Neural Processing Letters

Volumn 17, Issue 1, 2003, Pages 85-91

  Hanna, Andrew I ^a   Mandic, Danilo P ^b  

^a UNIVERSITY OF EAST ANGLIA   (United Kingdom)

^b IMPERIAL COLLEGE LONDON   (United Kingdom)

Author keywords

Complex valued nonlinear adaptive filter; Data reusing; Normalised complex non linear gradient descent

Indexed keywords

ALGORITHMS; NEURAL NETWORKS; NONLINEAR SYSTEMS;

NEURAL ADAPTIVE FILTERS;

ADAPTIVE FILTERING;

EID: 0037314819     PISSN: 13704621     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1022915613633     Document Type: Article

References (12)

1. Widrow, B., McCool, J. and Ball, M.: 'The complex LMS algorithm,' Proceedings of the IEEE 63 (1975), 719-720.
2. Hirose, A.: 'Continuous complex-valued backpropagation learning,' Electronics Letters 28(20) (1992), 1854-1855.
3. Georgiou, G. M. and Koutsougeras, C.: 'Complex domain backpropagation,' IEEE Transactions on Circuits and Systems - II: Analog and Digital Signal Processing 39(5) (1992), 330-334.
4. Benvenuto, N. and Piazza, F.: 'On the complex backpropagation algorithm,' IEEE Transactions on Signal Processing 40(4) (1992), 967-969.
5. Hanna, A. I., Mandic, D. P. and Razaz, M.: 'A normalised backpropagation learning algorithm for multilayer feed-forward neural adaptive filters,' Proceedings of the XI IEEE Workshop on Neural Networks for Signal Processing, 2001, pp. 63-72.
6. Mandic, D. P., Hanna, A. I. and Razaz, M. 'A normalized gradient descent algorithm for nonlinear adaptive filters using a gradient adaptive step size,' IEEE Signal Processing Letters, 8(11) (2001), 1-3.
7. Mandic, D. P. and Chambers, J. A.: Recurrent Neural Networks for Prediction, John Wiley and Sons, 2001.
8. Roy, S. and Shynk, J.: 'Analysis of the data-reuse LMS algorithm,' Proceedings of the 32nd Midwest Symposium on Circuits and Systems, 2, 1989, pp. 1127-1131.
9. Mandic, D. P. and Chambers, J. A.: 'A posteriori real time recurrent learning schemes for a recurrent neural network based non-linear predictor,' IEE Proceedings - Vision, Image and Signal Processing, 145(6), 1998, pp. 365-370.
10. Mandic, D. P. and Chambers, J. A.: 'Relations between the a priori and a posteriori errors in nonlinear adaptive neural filters,' Neural Computation, 12(6) (2000), 1285-1292.
11. Douglas, S.: 'A family of normalized LMS algorithms,' IEEE Signal Processing Letters, 1(3) (1994), 49-51.
12. Narendra, K. S. and Parthasarathy, K.: 'Identification and control of dynamical systems using neural networks,' IEEE Transactions on Neural Networks, 1(1) (1990), 4-27.