Home   >   CSC-OpenAccess Library   >    Manuscript Information
Optimization RBFNNs Parameters using Genetic Algorithms: Applied on Function Approximation
Mohammed Awad
Pages - 295 - 307     |    Revised - 30-06-2010     |    Published - 10-08-2010
Volume - 4   Issue - 3    |    Publication Date - July 2010  Table of Contents
Radial Basis Function Neural Networks, Genetic Algorithms, Function Approximation.
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.
CITED BY (9)  
1 Suja, K. R., & Raglend, J. (2015). Cuckoo search (CS)-NFC-based UPQC for compensating voltage sag of nonlinear load. Journal of Experimental & Theoretical Artificial Intelligence, (ahead-of-print), 1-18.
2 Xiong, X., Feng, J., & Jiang, L. (2015, October). Automatic digital modulation classification for ORS satellite relay communication. In Wireless Communications & Signal Processing (WCSP), 2015 International Conference on (pp. 1-5). IEEE.
3 Samanta, C. K., Hota, M. K., Nayak, S. R., Panigrahi, S. P., & Panigrahi, B. K. (2014). Energy management in hybrid electric vehicles using optimized radial basis function neural network. International Journal of Sustainable Engineering, 7(4), 352-359.
4 Saehana, S., Iskandar, F., & Abdullah, M. (2013). Optimization of electrospinning parameter by employing genetic algorithm in order to produce desired nanofiber diameter. World Academy of Science, Engineering and Technology, International Journal of Chemical, Molecular, Nuclear, Materials and Metallurgical Engineering, 7(1), 78-83.
5 Suja, K. R., & Raglend, I. J. (2013). Adaptive genetic algorithm/neuro-fuzzy logic controller based unified power quality conditioner controller for compensating power quality problems. Australian Journal of Electrical and Electronics Engineering, 10(3), 351-361.
6 Barcena-Humanes, J. L., Mata-Moya, D., Jarabo-Amores, M. P., del Rey-Maestre, N., & Martin de Nicolas-Presa, J. (2013, June). Analysis of NNs Detectors for Targets with Unknown Correlation in Gaussian Interference. In Computational Intelligence, Communication Systems and Networks (CICSyN), 2013 Fifth International Conference on (pp. 48-53). IEEE.
7 Zamani, A., Sorbi, M. R., & Safavi, A. A. (2013). Application of neural network and ANFIS model for earthquake occurrence in Iran. Earth Science Informatics, 6(2), 71-85.
8 Patheja, P. S., Waoo, A. A., & Sharma, R. (2012). Comparison of Efficient and Rand Index Fitness Function for Clustering Gene Expression Data. In Advances in Computer Science and Information Technology. Computer Science and Information Technology (pp. 160-167). Springer Berlin Heidelberg.
9 Mata Moya, D. A. D. L. (2012). Diseño de detectores robustos en aplicaciones radar.
1 Google Scholar 
2 Academic Journals Database 
3 CiteSeerX 
4 iSEEK 
5 Socol@r  
6 ResearchGATE 
7 Libsearch 
8 Bielefeld Academic Search Engine (BASE) 
9 Scribd 
10 WorldCat 
11 SlideShare 
13 PdfSR 
A. F. Sheta and K. D. Jong. “Time-series forecasting using GA-tuned radial basis functions”. Information Sciences, Special issue, 2001.
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.
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.
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.
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)
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)
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.
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.
Fogel L.J., Owens A.J. and Walsh M.J. “Artificial Intelligence through Simulated Evolution”. John Wiley & Sons, 1966.
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
González. J, “Identificación y optimización de redes de funciones de base radiales para aproximación funcional”. PhD Thesis. University of Granada. 2001.
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.
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.
M. J. D. Powell. “The Theory of Radial Basis Functions Approximation, in Advances of Numerical Analysis”. pp. 105–210, Oxford: Clarendon Press, 1992.
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.
P. T. Rodríguez-Piñero. “Introducción a los algoritmos genéticos y sus aplicaciones”. Universidad Rey Juan Carlos, España, Madrid. (2003)
Ph. Koehn. “Combining Genetic Algorithms and Neural Networks”. Master Thesis University of Tennessee, Knoxville, December 1994.
Rivas. A. “Diseño y optimización de redes de funciones de base radial mediante técnicas bioinspiradas”. .PhD Thesis. University of Granada. 2003.
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.
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
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.
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.
Y. Hwang and S. Bang. “An efficient method to construct a radial basis function neural network classifier”. Neural Networks, 10(8):1495–1503, 1997.
Z. Michalewickz. Univ. of North Carolina, Charlotte “Genetic Algorithms + Data Structures = Evolution Programs”. Springer-Verlag London, UK (1999).
Z. Zainuddin O. Pauline. “Function approximation using artificial neural networks”. 12th WSEAS International Conference on Applied Mathematics, 2007 Cairo, Egypt pp: 140-145.
Dr. Mohammed Awad
Arab American University - Palestinian Occupied