Assessing the noise immunity and generalization of radial basis function networks

Neural Processing Letters

Volumn 18, Issue 1, 2003, Pages 35-48

  Bernier, J L ^a   Diaz A F ^a   Fernandez F J ^a   Canas A ^a   Gonzalez J ^a   Martin Smith P ^a   Ortega, J ^a  

^a UNIVERSITY OF GRANADA   (Spain)

Author keywords

Generalization; Mean square error degradation; Noise immunity; Perturbation models; Radial basis function

Indexed keywords

LEARNING ALGORITHMS; LEARNING SYSTEMS; MATHEMATICAL MODELS; PERTURBATION TECHNIQUES;

MEAN SQUARE ERROR DEGRADATION; MEAN SQUARED SENSITIVITY; PERTURBATION MODELS;

RADIAL BASIS FUNCTION NETWORKS;

EID: 0344897166     PISSN: 13704621     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1026275522974     Document Type: Article

References (21)

1. Broomhead, D. S. and Lowe, D.: Multivariate functional interpolation and adaptive networks, Complex Systems, 2 321-355.
2. Haykin, S.: Neural Networks. A Comprehensive Foundation, (second edition, Prentice Hall, 1999).
3. Hochreiter, H. and Schmidhuber, J.: Flat Minima, Neural Computation, 9 (1997), 1-42.
4. Minai, A. A. and Williams, R. D.: Perturbation response in feedforward networks, Neural Networks, 7(5) (1994), 783-796.
5. Edwards, P. J. and Murray, A. F.: Towards optimally distributed computation, Neural Computation, 10 (1998), 997-1015.
6. Bernier, J. L., Ortega, J., Rodriguez, M. M., Rojas, I. and Prieto, A.: An accurate measure for multilayer perceptron tolerance to weight deviations, Neural Processing Letters, 10(2) (1999), 121-130.
7. Bernier, J. L., Ortega, J., Ros, E., Rojas, I. and Prieto, A.: A quantitative study of fault tolerance, noise immunity and generalization ability of MLPs, Neural Computation, 12 (2000); 2941-2964.
8. Segee, B. E. and Carter, M. J.: Comparative fault tolerance of generalized radial basis function and multilayer perceptron networks, IEEE Int. Conference on Neural Networks, 3 (1993), 1847-1852.
9. Townsend, N. W. and Tarassenko, L.: Estimations of error bounds for neural-network function approximators, IEEE Trans. on Neural Networks, 10(2) (1999), 217-230.
10. Parra, X. and Catala, A.: Fault tolerance in the learning algorithm of radial basis function networks, Proc. IEEE-INNS-ENNS Int. Joint Conference on Neural Networks, 3 (2000), 527-532.
11. Hagiwara, K., Hayasaka, T., Toda, N., Usui, S. and Kuno, K.: Upper bound of the expected trainig error of neural regression for a gaussian noise sequence, Neural Networks, 14 (2001), 1419-1429.
12. Catala, A. and Parra, X.: Fault tolerance parameter model of radial basis function networks, IEEE Int. Conference on Neural Networks, 2 (1996), 1384-1389.
13. Hegde, M. V., Naraghi-Pour, M. and Bapat, P.: Learning algorithms for fault tolerance in radial basis function networks, Proc 37th Midwest Symposium on Circuits and Systems, 1 (1995), 535-538.
14. Wang, Z. and Zhu, T.: An efficient learning algorithm for improving generalization performance of radial basis function neural networks, Neural Networks, 13 (2000) 545-553.
15. Bernier, J. L., Ortega, J., Rojas, I., Ros, E. and Prieto, A.: Obtaining fault tolerant multi-layer perceptrons using an explicit regularization. Neural Processing Letters. 12(2) (2000), 107-113.
16. Choi, J. Y. and Choi, C.: Sensitivity analysis of multilayer perceptron with differentiable activation functions, IEEE Trans. on Neural Networks, 3(1) (1992), 101-107.
17. Bishop, C.: Training with noise is equivalent to Tikhonov regularization, Neural Computation, 7(1) (1995), 108-116
18. Mackey, M. C. and Glass, L.: Oscillation and chaos in physiological control systems, Science, 197 (1977), 287-289.
19. Cherkassky, V., Gehring, D. and Mulier, F.: Comparison of adaptive methods for function estimation from samples, IEEE Trans. on Neural Networks, 7(4) (1996), 969-984.
20. Box, G. E. P. and Jenkins, G. M.: Time Series Analysis, Forecasting and Control. San Francisco, Holden-Day, CA, 2nd ed., 1976.
21. Gonzalez, J., Rojas, I., Pomares, H., Fernandez, F. J. and Diaz, A. F.: Multiobjective evolutionary optimization of the size, shape and position parameters of radial basis function networks for function approximation, IEEE Trans. on Neural Networks (in press).