A fast U-D factorization-based learning algorithm with applications to nonlinear system modeling and identification

IEEE Transactions on Neural Networks

Volumn 10, Issue 4, 1999, Pages 930-938

  Zhang, Youmin ^a,b   Li, X Rong ^c  

^a UNIVERSITY OF WESTERN ONTARIO   (Canada)

^b NORTHWESTERN POLYTECHNICAL UNIVERSITY   (China)

^c UNO College of Engineering   (United States)

Author keywords

BP algorithm; Extended Kalman filter; Feedforward neural networks; Forgetting factor; U D factorization

Indexed keywords

BACKPROPAGATION; COMPUTER SIMULATION; CONVERGENCE OF NUMERICAL METHODS; FEEDFORWARD NEURAL NETWORKS; KALMAN FILTERING; MATHEMATICAL MODELS; MULTILAYER NEURAL NETWORKS; PARAMETER ESTIMATION; RANDOM PROCESSES;

FADING MEMORY EXTENDED KALMAN FILTER; FORGETTING FACTOR; U-D FACTORIZATION;

LEARNING ALGORITHMS;

EID: 0032681148     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/72.774266     Document Type: Article

References (13)

1. G. J. Bierman, Factorization Methods for Discrete Sequential Estimation. New York: Academic, 1977.
2. S. A. Billings, H. B. Jamaluddin, and S. Chen, "Properties of neural networks with applications to modeling nonlinear dynamical systems," Int. J. Contr., vol. 55, no. 1, pp. 193-224, 1992.
3. G. Chen and H. Ogmen, "Modified extended Kalman filtering for supervised learning," Int. J. Syst. Sci., vol. 24, no. 6, pp. 1207-1214, 1993.
4. Y. Iiguni, H. Sakai, and H. Tokumaru, "A real-time learning algorithm for a multilayered neural network based on the extended Kalman filter," IEEE Trans. Signal Processing, vol. 40, no. 4, pp. 959-966, 1992.
5. L. Ljung and T. Soderstrom, Theory and Practice of Recursive Identification. Cambridge, MA: MIT Press, 1983.
6. K. S. Narendra and K. Parthasarathy, "Identification and control of dynamical systems using neural networks," IEEE Trans. Neural Networks, vol. 1, pp. 4-27, 1990.
7. D. Obradovic, "On-line training of recurrent neural networks with continuous topology adaptation," IEEE Trans. Neural Networks, vol. 7, pp. 222-228, 1996.
8. G. Puskorius and L. A. Feldkamp, "Neurocontrol of nonlinear dynamical systems with Kalman filter trained recurrent networks," IEEE Trans. Neural Networks, vol. 5, pp. 279-297, 1994.
9. S. Shah, F. Palmieri, and M. Datum, "Optimal filtering algorithms for fast learning in feedforward neural networks," Neural Networks, vol. 5, pp. 779-787, 1992.
10. S. Singhal and L. Wu, "Training feedforward networks with the extended Kalman algorithm," in Proc. Int. Conf. ASSP, 1989, pp. 1187-1190.
11. Y. M. Zhang and X. R. Li, "A fast U-D factorization-based recursive prediction error learning algorithm for feedforward neural networks," in Proc. 35th IEEE Conf. Decision Contr., Kobe, Japan, Dec. 1996, pp. 2036-2041.
12. _, "A hybrid training algorithm for RBF networks with application to modeling and identification of nonlinear systems," in Proc. 35th IEEE Conf. Decision Contr., Kobe, Japan, Dec. 1996, pp. 937-942.
13. _, "Helicopter parameter identification using functional-link neural net," in Proc. 2nd Chinese World Congr. Intell. Contr. Intell. Automat., Xian, PR China, June 1997.