Noise reduction in state space using the focused gamma neural network

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn 2, Issue , 1994, Pages II533-II536

  Principe, Jose C ^a   Kuo, Jyh Ming ^a  

^a UNIVERSITY OF FLORIDA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

SIGNAL PROCESSING; WHITE NOISE;

ADAPTIVE MEMORY; BROADBAND SIGNAL; GAMMA MODELS; NEURAL MODELING; NOISY SIGNALS; NONLINEAR PREDICTORS; SIGNAL SUB-SPACE;

SIGNAL TO NOISE RATIO;

EID: 84900612239     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.1994.389601     Document Type: Conference Paper

References (8)

1. Packard, N.H., J.P. Crutchfield, J.D. Farmer, and R.S. Shaw, Geometry from a time series, Phys. Rev. Lett., vol 45, no. 9, pp. 712-716.1980
2. Widrow B., Steams S., Adaptive Signal Processing, Prentice Hall 1985.
3. de Vries, B. and J.C. Principe, The gamma model - a new neural model for temporal processing, Neural Nerworks, vol5, pp. 565-576, 1992.
4. Fraser A., Swinney H., Independent coordinates for strange attractors from mutual information", Phys. Rev. A, vol33., 11341140,1986.
5. Takens, F., Detecting strange attractors, In: Dynamical systems and turbulence (edited by D.A. Rand, and L.S. Yang), Lecture Nures in Mathemarics, vol. 898, Berlin: Springer, 1981
6. Landa, P.S., and M.G. Rosenblum, Time series analysis for system identification and diagnostics, Physica D 48, 232-254,1991.
7. Marple, S. L. Jr., Digital Spectral Analysis with Applications, Prentice-Hall, NJ, 1987.
8. Williams R., Zipser D., A learning algorithm for continuously running fully recurrent networks, in Neural Computarion, vol 1, #2, 1989.