On-line RBFNN based identification of rapidly time-varying nonlinear systems with optimal structure-adaptation

Mathematics and Computers in Simulation

Volumn 63, Issue 1, 2003, Pages 1-13

  Apostolikas, Giorgos ^a   Tzafestas, Spyros ^a  

^a NATIONAL TECHNICAL UNIVERSITY OF ATHENS   (Greece)

Author keywords

Adaptive systems modeling; Radial basis function networks; Time varying system tracking

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; NONLINEAR SYSTEMS; ONLINE SYSTEMS; TIME VARYING SYSTEMS;

TIME VARYING SYSTEM TRACKING;

RADIAL BASIS FUNCTION NETWORKS;

EID: 0037451862     PISSN: 03784754     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0378-4754(02)00159-3     Document Type: Article

References (17)

1. Broomhead D.S., Lowe D. Multivariable function interpolation and adaptive networks. Complex Syst. 2:1988;321-355.
2. Hartman E.J., Keeler J.D., Kowalski J.M. Layered neural networks with Gaussian hidden units as universal approximators. Neural Comput. 2:1990;210-215.
3. Karayiannis N.B. Reformulated radial basis neural networks trained by gradient descent. IEEE Trans. Neural Netw. 10(3):1999;657-671.
4. Krzyzak A., Linder T. Radial basis function networks and complexity regularization in function learning. IEEE Trans. Neural Netw. 9:1998;247-256.
5. C.T. Lin, Neural Fuzzy Control Systems with Structure and Parameter Learning, World Scientific, Singapore, 1994.
6. Moody J., Darken C.J. Fast learning in locally-tuned processing units. Neural Comput. 1:1989;281-294.
7. Niyogi P., Girosi F. On the relationship between generalization error, hypothesis complexity, and sample complexity for radial basis functions. Neural Comput. 8:1996;819-842.
8. Park D.C., El-Sharkawi M.A., Marks R.J. II An adaptively trained neural network. IEEE Trans. Neural Netw. 2(3):1991;334-345.
9. Park J., Sandberg W. Universal approximation using radial-basis-function networks. Neural Comput. 3:1991;246-257.
10. Park J., Sandberg I.W. Universal approximation and radial basis function networks. Neural Comput. 5:1993;305-316.
11. Ruiz de Angulo V., Torras C. On-line learning with minimal degradation in feedforward networks. IEEE Trans. Neural Netw. 6(3):1995;657-668.
12. S.G. Tzafestas, Y. Anthopoulos, Supervised learning in multilayer perceptrons: the back-propagation algorithm, in: S.G. Tzafestas (Ed.), Soft Computing in Systems and Control Technology, World Scientific, Singapore, 1999, pp. 3-30.
13. Tzafestas S.G., Rigatos G.G. Self tuning multivariable fuzzy and neural control using genetic algorithms. J. Inform. Optimiz. Sci. 21(2):2000;257-287.
14. Tzafestas S.G., Skoundrianos E.N., Rigatos G.G. Nonlinear neural control of discrete-time systems using local model neural networks. Int. J. Knowledge-Based Intell. Eng. Syst. 4(2):2000;130-140.
15. S.G. Tzafestas, D. Vogiatzis, Concerning Hopfield networks: an overview with application to system identification and control, in: S.G. Tzafestas (Ed.), Computational Intelligence in Systems and Control Design and Applications, Kluwer, Dordrecht, 1999, pp. 311-334.
16. Xu L., Krzyzak A., Yuille A. On radial basis function nets and kernel regression: approximation ability, convergence rate and receptive field size. Neural Netw. 7:1994;609-628.
17. Yingwei L., Sundararajan N., Saratchandran P. Identification of time-varying nonlinear systems using minimal radial basis function networks. IEE Proc. Control Theory Appl. 144(2):1997;202-208.