Convergent decomposition techniques for training RBF neural networks

Neural Computation

Volumn 13, Issue 8, 2001, Pages 1891-1920

  Buzzi, C ^a   Grippo, L ^a   Sciandrone, M ^b  

^a SAPIENZA UNIVERSITY OF ROME   (Italy)

^b CNR   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; BIOLOGICAL MODEL; LEARNING; MATHEMATICS;

ALGORITHMS; LEARNING; MATHEMATICS; MODELS, NEUROLOGICAL; NEURAL NETWORKS (COMPUTER);

EID: 0035434383     PISSN: 08997667     EISSN: None     Source Type: Journal    
DOI: 10.1162/08997660152469396     Document Type: Article

References (13)

1. Bertsekas, D. P. (1999). Nonlinear programming. Boston: Athena Scientific.
2. Bishop, C. M. (1995). Neural networks for pattern recognition. Oxford: Clarendon Press.
3. Buzzi, C., Grippo, L., & Sciandrone, M. (1999). Convergent decomposition techniques for training RBF neural networks (Tech. Rep. No. IASI 504). Istituto di Analisi dei Sistemi ed Informatica.
4. Gill, P. E., & Murray, W. (1979). Conjugate-gradient methods for large-scale nonlinear optimization (Tech. Rep. No. SOL 79-15). Department of Operations Research, Stanford University.
5. Gill, P. E., Murray, W., & Wright, M. H. (1981). Practical Optimization. London: Academic Press.
6. Grippo, L., & Sciandrone, M. (1999). Globally convergent block-coordinate techniques for unconstrained optimization. Optimization Methods and Software, 10, 587-637.
7. Grippo, L., & Sciandrone, M. (2000). On the convergence of the block nonlinear Gauss-Seidel method under convex constraints. Operations Research Letters, 26, 127-136.
8. Haykin, S. (1999). Neural networks. Englewood Cliffs, NJ: Prentice Hall.
9. Moody, J., & Darken, L. W. (1989). Fast learning in networks of locally-tuned processing units. Neural Computation, 1, 281-294.
10. Poggio, T., & Girosi, F. (1990). Networks for approximation and learning. Proceedings of the IEEE, 78, 1481-1497.
11. Powell, M. J. D. (1973). On search directions for minimization algorithms. Mathematical Programming, 4, 193-201.
12. Preschelt, L. (1994). Proben 1 - a set of neural network benchmark problems and bench-marking rules (Tech. Rep. No. 21/94). Karlsruhe, Germany: Fakultät für Informatic, Universität Karlsruhe.
13. Wettschereck, D., & Dietterich, T. (1992). Improving the performance of radial basis function networks by learning locations. In J. E. Moody, S. J. Hanson, & R. P. Lippman (Eds.), Advances in neural information processing systems, 4 (pp. 1133-1140).