|
| Optimization RBFNNs Parameters using Genetic Algorithms: Applied on Function Approximation
|
|
Full
text: |
 PDF(793.5KB) |
|
|
Source |
International Journal of Computer Science and Security (IJCSS) |
|
Table of Contents |
|
|
Download
Complete Issue PDF(4.51MB) |
|
Volume: 4 Issue: 3 |
| |
Pages: 265-372 |
|
Publication
Date: July 2010 |
|
ISSN
(Online): 1985-1553 |
|
|
|
|
|
Pages |
295 - 307 |
|
Author(s) |
|
|
|
Published
Date |
10-08-2010 |
|
Publisher |
CSC
Journals, Kuala Lumpur,
Malaysia |
|
ADDITIONAL
INFORMATION |
| Keywords Abstract References Cited by Related Articles Collaborative
Colleague |
| |
|
| |
KEYWORDS: Radial Basis Function Neural Networks, Genetic Algorithms, Function Approximation. |
|
|
| |
|
|
| This Manuscript is indexed in the following databases/websites:- |
|
| 1. Directory of Open Access Journals (DOAJ) |
| 2. Scribd |
| 3. PDFCAST |
| 4. Docstoc |
| 5. Google Scholar |
| 6. WorldCat |
| 7. iSEEK |
| 8. Socol@r |
| 9. Bielefeld Academic Search Engine (BASE) |
| 10. ResearchGATE |
| 11. Academic Journals Database |
| 12. Libsearch |
| 13. slideshare |
| |
|
| |
|
|
| This paper deals with the problem of function approximation from a given set of input/output (I/O) data. The problem consists of analyzing training examples, so that we can predict the output of a model given new inputs. We present a new approach for solving the problem of function approximation of I/O data using Radial Basis Function Neural Networks (RBFNNs) and Genetic
Algorithms (GAs). This approach is based on a new efficient method of optimizing RBFNNs parameters using GA, this approach uses GA to optimize
centers c and radii r of RBFNNs, such that each individual of the population represents centers and radii of RBFNNs. Singular value decomposition (SVD) is used to optimize weights w of RBFNNs. The GA initial population performed by using Enhanced Clustering Algorithm for Function Approximation (ECFA) to initialize the RBF centers c and k-nearest neighbor to initialize the radii r. The performance of the proposed approach has been evaluated on cases of one and two dimensions. The results show that the function approximation
using GA to optimize RBFNNs parameters can achieve better normalized-root- mean square-error than those achieved by traditional algorithms. |
| |
|
| |
|
| |
| 1 |
M. J. D. Powell. “The Theory of Radial Basis Functions Approximation, in Advances of Numerical Analysis”. pp. 105–210, Oxford: Clarendon Press, 1992.
|
|
|
| 2 |
Z. Zainuddin O. Pauline. “Function approximation using artificial neural networks”. 12th WSEAS International Conference on Applied Mathematics, 2007 Cairo, Egypt pp: 140-145.
|
|
|
| 3 |
B. Carse, A.G. Pipe, T.C. Forgarty and T. Hill, "Evolving radial basis function neural networks using a genetic algorithm", IEEE International Conference on Evolutionary Computation, Vol. 1, page 300 (1995)
|
|
|
| 4 |
B. Carse, A.G. Pipe, T.C. Forgarty and T. Hill, "Evolving radial basis function neural networks using a genetic algorithm", IEEE International Conference on Evolutionary Computation, Vol. 1, page 300 (1995)
|
|
|
| 5 |
D. Schaffer, D. Whitley and L.J. Eshelman, “Combinations of genetic algorithms and neural networks”. A survey of the state of the art, in Combinations of Genetic Algorithms and Neural Networks, pp. 1-37, IEEE Computer Society Press, 1992.
|
|
|
| 6 |
D. Prados. “A fast supervised learning algorithm for large multilayered neural networks”. in Proceedings of 1993 IEEE International Conference on Neural Networks, San Francisco, v.2, pp.778-782, 1993.
|
|
|
| 7 |
A. Topchy, O. Lebedko, V. Miagkikh, “Fast Learning in Multilayered Neural Networks by Means of Hybrid Evolutionary and Gradient Algorithm”. in Proc. of the First Int. Conf. on Evolutionary Computations and Its Applications, ed. E. D. Goodman et al., (RAN, Moscow), pp.390–399, 1996.
|
|
|
| 8 |
B. A. Whitehead and T.D. Choate. “Cooperative - Competitive Genetic Evolution of Radial Basis Function Centers and Widths for Time Series Predictio”. IEEE Transactions on Neural Networks, vol. 7, no. 8, pp.869-880, 1996.
|
|
|
| 9 |
Fogel L.J., Owens A.J. and Walsh M.J. “Artificial Intelligence through Simulated Evolution”. John Wiley & Sons, 1966.
|
|
|
| 10 |
M. W. Mak and K. W. Cho. “Genetic evolution of radial basis function centers for pattern classification”. In Proc. Of The 1998 IEEE International Joint Conference on Neural Networks, pages 669 – 673, 1998. Volume 1.
|
|
|
| 11 |
A. F. Sheta and K. D. Jong. “Time-series forecasting using GA-tuned radial basis functions”. Information Sciences, Special issue, 2001.
|
|
|
| 12 |
M. Awad, H. Pomares, F. Rojas, L.J. Herrera, J. González, A. Guillén. “Approximating I/O data using Radial Basis Functions:A new clustering-based approach”. IWANN 2005, LNCS 3512, pp. 289– 296, 2005.© Springer-Verlag Berlin Heidelberg 2005.
|
|
|
| 13 |
S. Chen, Y. Wu, and B. L. Luk. “Combined genetic algorithm optimization and regularized orthogonal least squares learning for radial basis function networks”. IEEE-NN, 10(5):1239, September 1999.
|
|
|
| 14 |
B. Burdsall and C. Giraud-Carrier. “GA-RBF: A selfoptimising RBF network”. In Proc. of the Third International Conference on Artificial Neural Networks and Genetic Algorithms, pages 348–351. Springer-Verlag, 1997.
|
|
|
| 15 |
Y. Hwang and S. Bang. “An efficient method to construct a radial basis function neural network classifier”. Neural Networks, 10(8):1495–1503, 1997.
|
|
|
| 16 |
M. Awad, H. Pomares, I. Rojas, Member, IEEE. “Enhanced Clustering Technique in RBF Neural Network for Function Approximation”. INFOS2007, Fifth International Conference 24-26 March 2007, Cairo University Post Office, Giza, Egypt.
|
|
|
| 17 |
T. Hatanaka, N. Kondo and K. Uosaki. “Multi–Objective Structure Selection for Radial Basis Function Networks Based on Genetic Algorithm”. Department of Information and Physical Science Graduate School of Information Science and Technology, Osaka University 2–1 YamadaOka, Suita, 565–0871, Japan.
|
|
|
| 18 |
P. T. Rodríguez-Piñero. “Introducción a los algoritmos genéticos y sus aplicaciones”. Universidad Rey Juan Carlos, España, Madrid. (2003)
|
|
|
| 19 |
Z. Michalewickz. Univ. of North Carolina, Charlotte “Genetic Algorithms + Data Structures = Evolution Programs”. Springer-Verlag London, UK (1999).
|
|
|
| 20 |
Gonzalez, J.; Rojas, H.; Ortega, J.; Prieto, A. “A new clustering technique for function approximation”. Neural Networks, IEEE .Transactions on, Volume: 13 Issue: 1, Jan. 2002. Page(s): 132 -142. “Conditional fuzzy C-means,” Pattern Recognition Lett., vol. 17, pp. 625–632, 1996
|
|
|
| 21 |
Rivas. A. “Diseño y optimización de redes de funciones de base radial mediante técnicas bioinspiradas”. .PhD Thesis. University of Granada. 2003.
|
|
|
| 22 |
González. J, “Identificación y optimización de redes de funciones de base radiales para aproximación funcional”. PhD Thesis. University of Granada. 2001.
|
|
|
| 23 |
Ph. Koehn. “Combining Genetic Algorithms and Neural Networks”. Master Thesis University of Tennessee, Knoxville, December 1994.
|
|
|
| 24 |
Sambasiva, R. Baragada, S. Ramakrishna, M.S. Rao, S. P. “Implementation of Radial Basis Function Neural Network for Image Steganalysis”, International Journal of Computer Science and Security, Vol. 2, Issue 1, pp. 12 – 22, March 2008
|
|
|
| 25 |
Sufal D. Banani Saha, “Data Quality Mining using Genetic Algorithm”, International Journal of Computer Science and Security, ISSN: 1985-1553, 3(2): pp 105-112, 2009.
|
|
|
| |
|
| |
|
| |
| |
|
| |
|
| |
| 1 |
Search-Document
|
| |
|
| |
|
| |
|
| Mohammed Awad : Colleagues
|
|
