[1] Bishop C., Neural Networks for Pattern Recognition. Oxford University Press, USA, 1995. Google Scholar

[2] Callan R., The Essence of Neural Networks. Prentice Hall PTR Ur Saddle River, NJ, USA, 1998. Google Scholar

[3] Haykin S., Neural Networks: A Comprehensive Foundation Neural Networks. 2nd Edition, Prentice Hall, Upper Saddle River, NJ, 1999. Google Scholar

[4] Acosta F.M.A., Radial Basis Functions and Related Models: An Overview. Signal Processing, 45(1), 37-58, 1995. CrossrefGoogle Scholar

[5] Bors A.G., Introduction of the Radial Basis Function Networks. Online Symposium for Electronics Engineers, 1(1), 1-7, 2001. Google Scholar

[6] Buhmann M.D., Multivariate Cardinal Interpolation with Radial Basis functions. Constructive Aoximation, 6(3), 225-255, 1990. Google Scholar

[7] Buhmann M.D., On Quasi-Interpolation with Radial Basis Functions. J. Aox.Theory, 72(1), 103-130, 1993a. Google Scholar

[8] Buhmann M. D., New Developments in the Theory of Radial Basis Function Interpolation. In Jette, K. and Utreras, F. (eds.),Multivariate Aoximation: From CAGD to Wavelets, World Scientific Publishing, Singapore, 1993b, 35-75. Google Scholar

[9] Buhmann M.D., Mehaute A.L., Knot Removal with Radial Basis Function Interpolation. C R Acad Science Paris Ser I Math., 320, 501-506, 1995. Google Scholar

[10] Buhmann M.D., Radial Functions on Compact Surt. Proceeding of Edinburgh Mathematical Society, 41(1), 33-46, 1998. Google Scholar

[11] Buhmann M.D., Radial Basis Functions. Acta Numerica Cambridge University Press, 9, 1-38, 2000. Google Scholar

[12] Buhmann M.D., Radial Basis Functions: Theory and Implementations. vol.12, Cambridge University Press University Press, Cambridge, 2003. Google Scholar

[13] Caiti A., Magenes G., Panisini T., Simpson R., Smooth Aoximation by Radial Basis Functions: three case studies. J. A. Sci. Comp, 1, 88-113, 1994. Google Scholar

[14] Wu S., Chow T.W.S., Induction Machine Fault Detection Using SOM-based RBF Neural Networks. IEEE Transactions on Industrial Electronics, 51(1), 183-194, 2004. CrossrefGoogle Scholar

[15] Powell M.J.D., Radial Basis Functions for Multivariable Interpolation: A Review. In: J.C. Mason, M.G. Cox (Eds.), Algorithms for the Aoximation, Clarendon Press, 1987a, 143-167. Google Scholar

[16] Powell M.J.D., Radial Basis Function Aoximations to Polynomials. In: D.F. Griths, G.A. Watson (Eds.), Numerical Analysis 87, Longman Publishing Group, Chicago, 1987b, 223-241. Google Scholar

[17] Powell M.J.D., The Theory of Radial Basis Function Aoximation in 1990. Advances in Numerical Analysis, 2, 105-210, 1992. Google Scholar

[18] Powell M.J.D., Recent Research at Cambridge on Radial Basis Functions. New Developments in Aoximation Theory, International Series of Numerical Mathematics, 215-232, 1999. Google Scholar

[19] Broomhead D.S., Lowe D., Multi-variable Functional Interpolation and Adaptive Networks. Complex Systems, 2, 321-355, 1988. Google Scholar

[20] Gyorfi L., Kohler M., Krzyzak A.,Walk H., A Distribution Free Theory of Non-Parametric Regression. Springer Verlag, New York Inc., 2002. Google Scholar

[21] Bishop C., Improving the Generalization Properties of Radial Basis Function Neural Networks. Neural Computation, 3(4), 579- 588, 1991. CrossrefGoogle Scholar

[22] Park J., Sandberg I.W., Universal Aoximation Using Radial Basis Function Networks. Neural Computation, 3(2), 246-257, 1991. CrossrefGoogle Scholar

[23] Beatson R.K., Cherrie J.B., Mouat C.T., Fast Fitting of Radial Basis Functions: Methods based on Preconditioned GMRES Iteration. Advances in Computer Mathematics, 11(2), 253-270, 1999. Google Scholar

[24] Patrikar A.M., AoximatingGaussian Mixture Model on Radial Basis Function Networks withMultilayer Perceptron. IEEE Transactions on Neural Networks, 24(7), 1161-1166, 2013. Google Scholar

[25] Mao K.Z., Huang G., Neuron Selection for RBF Neural Network Classifier Based on Data Structure Preserving Criterion. IEEE Transactions on Neural Networks, 15(6), 15531-1540, 2005. Google Scholar

[26] Beatson R.K., Newsam G.N., Fast Evaluation of Radial Basis Functions: I. Computers Math., 24(12), 7-19. 34. Bishop, C. (1991). Improving the Generalization Properties of Radial Basis Function Neural Networks. Neural Computation, 3(4), 579-588, 1992. Google Scholar

[27] Schwenker F., Kestler H.A., Palm G., Höher M., Similarities of LVQ and RBF Learning - A Survey of Learning Rules and the Aication to the Classification of Signals from High-Resolution Electrocardiography. Proceedings IEEE SMC, 1, 646-651, 1994. Google Scholar

[28] Falcao A., Langlois T., Wichert A., Flexible Kernels for RBF Networks. Neurocomputing, 69, 2356-2359, 2006. CrossrefGoogle Scholar

[29] Fernandez-Navarro F., Hervas-Martinez C., Cruz-Ramirez M., Gutierrez P.A., Valero A., Evolutionary q-Gaussian Radial Basis Function Neural Network to Determine the Microbial Growth/no Growth Interface of Staphylococcus Aureus. Aied Soft Computing, 11(3), 3012-3020, 2011. CrossrefGoogle Scholar

[30] Yamada T., Wrobel L.C., Properties of Gaussian Radial Basis Functions in the Dual Reciprocity Boundary Element Method. Zeitschrift for Angewandte Mathematik und Physik (ZAMP), 44(6), 1054-1067, 1993. Google Scholar

[31] Franke C., Schaback, R., Convergence Orders of Meshless Collocation Methods using Radial Basis Functions. Advances in Computational Mathematics, 8(4), 381-399, 1998. Google Scholar

[32] Baxter B.J.C., The Interpolation Theory of Radial Basis Functions. University of Cambridge, Cambridge, 1992. Google Scholar

[33] Bors A.G., Pitas, I., Object Classification in 3-D images using Alpha-trimmedMean Radial Basis Function Network. IEEE Transaction on Image Processing, 8(12), 1744-1756, 1999. Google Scholar

[34] Bors A.G., Pitas I., Median Radial Basis Functions Neural Network. IEEE Transactions on Neural Networks, 7(6), 1351-1364, 1996. Google Scholar

[35] Hon Y.C., Schaback R., Zhou, X., An Adaptive Greedy Algorithm for Solving Large RBF Collocation Problems. Numerical Algorithms, 32, 13-25, 2003. CrossrefGoogle Scholar

[36] Cherrie J.B., Beatson R.K., Newsam G.N., Fast Evaluation of Radial Basis Functions: Methods for GeneralizedMulti-quadrics in Rn. SIAM J Science Computation, 23(5), 1549-1571, 2002. Google Scholar

[37] Lim E.A., Zainuddin Z., An Improved Fast Training Algorithm for RBF Networks using Symmetry-Based Fuzzy C-Means Clustering. Matematika, 24(2), 141-148, 2008. Google Scholar

[38] Uykan Z., Guzelis C., Celebi M.E., Koivo H.N., Analysis of Input Output clustering for Determining Centers of RBFNs. IEEE Transactions on Neural Networks, 11(4), 851-858, 2000. Google Scholar

[39] Hunt K.J., Haas R., Murray-Smith R., Extending the Functional Equivalence of Radial Basis Function Networks and Fuzzy Inference Systems. IEEE Trans Neural Network, 7, 776-781, 1996. Google Scholar

[40] Gu Z., Li H., Sun Y., Chen Y., Fuzzy Radius Basis Function Neural Network Based Vector Control of Permanent Magnet Synchronous Motor. In Proceedings of IEEE International Conference on Mechatronics and Automation (ICMA 2008), 224-229, 2008. Google Scholar

[41] Kong F., Zhang Z., Liu Y., Selection of Logistics Service Provider Based on Fuzzy RBF Neural Networks. In Proceedings of IEEE International Conference on Automation and Logistics (ICAL 2008), 1189-1192, 2008. Google Scholar

[42] Li X., Dong J., Zhang Y., Modeling and Aying of RBF Neural Network Based on Fuzzy Clustering and Pseudo-Inverse Method. In Proceedings of International Conference on Information Engineering and Computer Science (ICIECS 2009), 1-4, 2009. Google Scholar

[43] Ziyang Z., ZhishengW., Yong H., Mingzhi G., Learning Method of RBF Network Based on FCM and ACO. In Proceedings of Control and Decision Conference (CCDC), 102-105, 2008. Google Scholar

[44] Kamalabady A.S., Salahshoor K., New SISO and MISO Adaptive Nonlinear Predictive Controllers Based on Self Organizing RBF Neural Networks. In Proceedings of 3rd International Symposium on Communications, Control, and Signal Processing (ISCCSP 2008), 703-708, 2008. Google Scholar

[45] Yang P., Zhu Q., Zhong X., Subtractive Clustering Based RBF Neural Network Model for Outlier Detection. J. Comput., 4(8), 755-762, 2009. Google Scholar

[46] Chen J.Y., Qin Z., Jia J., A PSO-Based Subtractive Clustering Technique for Designing RBF Neural Networks. In Proceedings of IEEE World Congress on Computational Intelligence (Evolutionary Computation’ 2008), 2047-2052, 2008. Google Scholar

[47] Liu H., He J., The Aication of Dynamic K-means Clustering Algorithm in the Center Selection of RBF Neural Networks. 3rd International Conference on Genetic and Evolutionary Computing, 2009. WGEC’09, 488-491, 2009. Google Scholar

[48] Sing J.K., Basu D.K., Nasipuri M., Kundu M., Improved k-means Algorithm in the Design of RBF Neural Networks. In Proceedings of TENCON Con Convergent Tech Asia-Pacific Region, 2, 841- 845, 2003. Google Scholar

[49] Zainuddin Z., Lye W.K., Improving RBF Networks Classification Performance by using K-Harmonic Means. World Academy of Science, Engineering and Technology, 62, 983-986, 2010. Google Scholar

[50] Huilan J., Ying G., Dongwei L., Jianqiang X., Self-adaptive Clustering Algorithm Based RBF Neural Network and its Aication in the Fault Diagnosis of Power Systems. In Proceedings of IEEE/PES Transmission and Distribution Conference and Exhibition: Asia and Pacific, Dalian, 1-6, 2005. Google Scholar

[51] Chen S., Hong X., Luk B.L., Harris C.J., Construction of Tunable Radial Basis Function Networks Using Orthogonal Forward Selection. IEEE Trans. Syst.Man. Cybern. B Cybern., 39(2), 457–466, 2009. CrossrefGoogle Scholar

[52] Chen S., Wang X.X., Hong X., Harris C.J., Kernel Classifier Construction Using Orthogonal Forward Selection And Boosting With Fisher Ratio Class Separability Measure. IEEE Trans. Neural Netw., 17(6), 1652–1656, 2006. CrossrefGoogle Scholar

[53] Hong X., A Fast Identification Algorithm for Box-Cox Transformation Based Radial Basis Function Neural Network. IEEE Trans. Neural Network, (17)4, 1064–1069, 2006. Google Scholar

[54] Wong Y.W., Seng K.P., Ang L.M., Radial Basis Function Neural Network with Incremental Learning for Face Recognition. IEEE Trans. Syst. Man. Cybern. B Cybern., 41(4), 940–949, 2011. CrossrefGoogle Scholar

[55] Yingwei L., Sundararajan N., Saratchandran P., A Sequential Learning Scheme for Function Aoximation using Minimal Radial Basis Function Neural Networks. Neural Computation, 9(2),461- 478, 1997. CrossrefGoogle Scholar

[56] Leonardis A., Bischof H., An Efficient MDL-Based Construction of RBF Networks. Neural Networks, 11(5), 963-973, 1998. CrossrefGoogle Scholar

[57] Alexandridis A., Chondrodima E., Sarimveis H., Radial Basis Function Network Training using a Nonsymmetric Partition of the Input Space and Particle Swarm Optimization. IEEE Transactions on Neural Networks and Learning Systems, 24(2), 219- 230, 2013. Google Scholar

[58] Orr, M., Optimising the Widths of Radial Basis Functions. In Proc. 5th Brazilian Symp. Neural Netw., 26–29, 1998. Google Scholar

[59] Yao W., Chen X., Zhao Y., Tooren M.V., Concurrent subspace width optimization method for RBF neural network modeling, IEEE Trans. Neural Netw. Learn. Syst., 23(2), 247–259, 2012. Google Scholar

[60] Yao W., Chen X., Tooren M.V., Wei Y., Euclidean Distance and Second Derivative Based Widths Optimization of Radial Basis Function Neural Networks. In Proc. Int. Joint Conf. Neural Netw., 18-23 July 2010, Barcelona, Spain, 1–8, 2010. Google Scholar

[61] Orr M.J., Regularization in the Selection of Radial Basis Function Centers. Neural Computation, 7(3), 606-623, 1995. CrossrefGoogle Scholar

[62] Su F.S., Chuang C.C., Tao C.W., Jeng J.J., Hsiao C.C., Radial Basis Function Networks with Linear Interval Regression Weights for Symbolic Interval Data. IEEE Trans. Syst. Man. Cybern. B Cybern., 42(1), 69–80, 2012. Google Scholar

[63] Zhang D., Deng L.F., Cai K.Y., So A., Fuzzy Nonlinear Regression with Fuzzified Radial Basis Function Network. IEEE Trans. Fuzzy Syst., 13(6), 742–760, 2005. CrossrefGoogle Scholar

[64] Langari R., Wang L., Yen J., Radial Basis Function Networks, Regression Weights, and the Expectation-Maximization Algorithm. IEEE Trans. Syst., Man., Cybern. A, Syst. Humans, 27(5), 613–623, 1997. CrossrefGoogle Scholar

[65] Andrieu C., Freitas N.D., Doucet A., Robust Full Bayesian Learning for Radial Basis Networks. Neural Comput., 13(10), 2359–2407, 2001. CrossrefGoogle Scholar

[66] Huan H.X., Hien D.T.T., Tue H.H., Efficient Algorithm for Training Interpolation RBF Networkswith Equally Spaced Nodes. IEEE Trans. Neural Netw., 22(6), 982–988, 2011. CrossrefGoogle Scholar

[67] Xie T., Yu H., Hewlett J., Rozycki P., Wilamowski B., Fast and Efficient Second-Order Method for Training Radial Basis Function Networks. IEEE Trans. Neural Netw. Learn. Syst., 23(4), 609–619, 2012. Google Scholar

[68] Gutiérrez P.A., Hervás-Martínez C., Martínez-Estudillo F.J., Logistic Regression by Means of Evolutionary Radial Basis Function Neural Networks. IEEE Trans. Neural Netw., 22(2), 246–263, 2011. CrossrefGoogle Scholar

[69] Sarimveis H., Alexandridis A., Mazarakis S., Bafas G., A New Algorithm for Developing Dynamic Radial Basis Function Neural Network Models Based on Genetic Algorithms. Comput. Chem. Eng., 28(1–2), 209–217, 2004. CrossrefGoogle Scholar

[70] González J., Rojas I., Pomares H., Ortega J., RBF Neural Networks, Multi-objective Optimization and Time Series Forecasting. LNCS, Springer-Verlag, 2084, 498–505, 2001. Google Scholar

[71] Gonzalez J., Rojas I., Ortega J., Pomares H., Fernandez F., Javier D., Antonio F., Multi-objective Evolutionary Optimization of the Size, Shape, and Position Parameters of Radial Basis Function Networks for Function Aoximation. IEEE Transactions on Neural Networks, 14(6), 1478-1495, 2003. Google Scholar

[72] Buchtala O., Klimek M., Sick B., Evolutionary Optimization of Radial Basis Function Classifiers for Data Mining Aications. IEEE Trans. Syst. Man. Cybern. B Cybern., 35(5), 928–947, 2005. CrossrefGoogle Scholar

[73] Valdez F., Melin P., Castillo O., Evolutionary Method Combining Particle Swarm Optimisation and Genetic Algorithms Using Fuzzy Logic for Parameter Adaptation and Aggregation: The Case Neural Network Optimisation for Face Recognition. Int. J. Artif. Intell. Soft Comput., 2(1–2), 77–102, 2010. CrossrefGoogle Scholar

[74] Valdez F., Melin P., Castillo O., An Improved Evolutionary Method with Fuzzy Logic for Combining Particle Swarm Optimization and Genetic Algorithms. A. Soft Comput., 11(2), 2625–2632, 2011. CrossrefGoogle Scholar

[75] Chen S., Hong X., Harris C.J., Particle Swarm Optimization Aided Orthogonal Forward Regression for Unified Data Modeling. IEEE Trans. Evol. Comput.,14(4), 477–499, 2010. CrossrefGoogle Scholar

[76] Huang D.S., Du J.X., A Constructive Hybrid Structure Optimization Methodology for Radial Basis Probabilistic Neural Networks. IEEE Trans. Neural Networks, 19(12), 2099–2115, 2008. CrossrefGoogle Scholar

[77] Oh S.K., Kim W.D., Pedrycz W., Joo S.C., Design Of K-Means Clustering-Based Polynomial Radial Basis Function Neural Networks (pRBF NNS) Realized with the Aid of Particle Swarm Optimization and Differential Evolution. Neurocomputing, 78(1), 121–132, 2012. CrossrefGoogle Scholar

[78] Hassan F.R., Koh S.P., Tiong S.K., Chong K.H., Abdalla A.N., Investigation of Induction Motor Parameter Identification using Particle Swarm Optimization based RBF Neural Network. International Journal of the Physical Sciences, 6, 4564-4570, 2011. Google Scholar

[79] Liang N.-Y., Huang G.-B., Saratchandran P., Sundararajan N., A Fast and Accurate Online Sequential Learning Algorithm for Feed-forward Networks. IEEE Trans. Neural Netw., 17(6), 1411–1423, 2006. CrossrefGoogle Scholar

[80] Cheu E.Y., Quek C., Ng S.K., ARPOP: An Atitive Reward-based Pseudo-Outer-Product Neural Fuzzy Inference System Inspired From The Operant Conditioning of Feeding Behavior in Aplysia. IEEE Trans. Neural Netw. Learn. Syst., 23(2), 317–329, 2012. Google Scholar

[81] Suresh S., Sundararajan N., Saratchandran P., A Sequential Multicategory Classifier Using Radial Basis Function Networks. Neurocomputing, 71(1), 1345–1358, 2008. CrossrefGoogle Scholar

[82] Huang G.B., Saratchandran P., Sundararajan N., An Efficient Sequential Learning Algorithm for Growing and Pruning RBF (GAPRBF) Networks. IEEE Transactions on Systems, Man, Cybernetics, 34(6), 2284-2292, 2004. Google Scholar

[83] Huang G., Saratchandran P., Sundararajan N., A Generalized Growing and Pruning RBF (GGAP-RBF) Neural Network for Function Aoximation. IEEE Transactions on Neural Networks, 16, 57- 67, 2005. Google Scholar

[84] Huang G.-B., Zhou H., Ding X., Zhang R., Extreme learning machine for regression and multiclass classification. IEEE Trans. Syst. Man Cybern. B Cybern., 42(2), 513–529, 2012. Google Scholar

[85] Kokshenev I., Braga A.P., An Efficient Multi-objective Learning Algorithm for RBF Neural Network, Neurocomputing, 73, 2799- 2808, 2010. CrossrefGoogle Scholar

[86] Dehuri S., Cho S.-B., Multi-Criterion Pareto Based Particle Swarm Optimized Polynomial Neural Network for Classification: A Review and State-of-the-Art. Computer Science Review, 3(1), 19-40, 2009. CrossrefGoogle Scholar

[87] Deb K., EvolutionaryMulti-criteria Optimization. In Proceedings of GECCO (Companion), 1155-1162, 2010. Google Scholar

[88] Coello C.A.C., Evolutionary Multi-objective Optimization. Wiley Interdisc. Rew.: Data Mining and Knowledge Discovery, 1(5), 444-447, 2011. Google Scholar

[89] Coello C.A.C., Dehuri S., Ghosh S., Swarm Intelligence forMultiobjective Problems in Data Mining (Eds.). Springer-Verlag, Berlin, Heidelberg, 2009. Google Scholar

[90] Gonzalez J., Rojas I., Pomares H., Ortega J., RBF Neural Networks, Multi-objective Optimization and Time Series Forecasting Connectionist Models of Neurons. Learning Processes, and Artificial Intelligence, 2084, 498-505, 2001. Google Scholar

[91] Hatanaka T., Kondo N., Uosaki K., Multi-objective Structure Selection for Radial Basis Function Networks Based on Genetic Algorithm. In Proceedings of Evolutionary Computation (CEC’03), 2, 1095-1100, 2003. Google Scholar

[92] Ferreira P.M., Ruano A.E., Fonseca C.M., Evolutionary Multiobjective Design of Radial Basis Function Networks for Green house Environmental Control. In Proceedings of the 16 the IFAC World Congress, 16(1), 870-870, 2005. Google Scholar

[93] Yen G.G., Multi-Objective Evolutionary Algorithm for Radial Basis Function Neural Network Design. Studies in Computational Intelligence (SCI), Springer-Verlag, 16, 221-239, 2009. Google Scholar

[94] Guillen A., Rojas I., Gonzalez J., Pomares H., Herrera L.J., Paechter B., Improving the Performance of Multi-objective Genetic Algorithm for Function Aoximation through Parallel Islands Specialization. In Proceedings of AI 2006, 1127-1132, 2006. Google Scholar

[95] Kondo N., Hatanaka T., Uosaki K., Pattern Classification by Evolutionary RBF Networks Ensemble Based onMulti-Objective Optimization. In Proceedings of International Joint Conference on Neural Networks, 2919-2925, 2006. Google Scholar

[96] Kondo N., Hatanaka T., Uosaki K., Nonlinear Dynamic System Identification Based on Multi objectively Selected RBF Networks. In Proceedings of the 2007 IEEE Symposium on Computational Intelligence in Multi criteria Decision Making, 112-127, 2007. Google Scholar

[97] Kokshenev I., Braga A.P., A Multi-objective Aoach to RBF Network Learning. Neurocomputing, 71, 1203-1209, 2008. CrossrefGoogle Scholar

[98] Qasem S.N., Shamsuddin S.M., Radial Basis Function Network Based on Time VariantMulti-objective Particle Swarm Optimization for Medical Diseases Diagnosis. Aied Soft Computing, 11(1), 1427-1438, 2011. CrossrefGoogle Scholar

[99] Qasem S.N., Shamsuddin S.M., Zain A.M., Multi-objective Hybrid Evolutionary Algorithms for Radial Basis Function Neural Network Design. Knowledge-based System, 27, 475-497, 2012. Google Scholar

[100] Alcalá-Fdez J., Sánchez L., García S., del Jesus M.J., Ventura S., Garrell J.M., Otero J., Romero C., Bacardit J., Rivas V.M., Fernández J.C., Herrera F., KEEL: A Software Tool to Assess Evolutionary Algorithms to Data Mining Problems. Soft Computing, 13, 307- 318, 2009. CrossrefGoogle Scholar

[101] Li X., On Simultaneous Aoximations by Radial Basis Function Neural Networks. A. Math. Comput., 95(1), 75-89, 1998. Google Scholar

[102] Light W.A., Wayne H., Error Estimates for Aoximation by Radial Basis Functions. Aoximation Theory, Wavelets, and Aications, 454, 215-246, 1995. Google Scholar

[103] LightW.,Wayne H., On Power Functions and Error Estimates for Radial Basis Function Interpolation. Journal of Aoximation Theory, 92(2), 245-266, 1998. Google Scholar

[104] Light W.A., Cheney E.W., Interpolation by Piecewise-Linear Radial Basis Functions II. Journal of Aoximation Theory, 64(1), 38- 54, 1991. Google Scholar

[105] Light W.A., Cheney E.W., Interpolation by Periodic Radial Basis Functions. Journal of Mathematical Analysis and Aications, 168(1), 111-130, 1992. Google Scholar

[106] Bellil W., Amar C.B., Alimi A.M., Comparison between Beta Wavelets Neural Networks, RBF Neural Networks and Polynomial Aoximation for 1D, 2D Functions Aoximation. Trans. Eng. Comp. Tech., 13, 102-107, 2006. Google Scholar

[107] Fornberg B., Driscoll T.A., Wright G., Charles R., Observations on the Behavior of Radial Basis Function Aoximations near Boundaries. Computers andMathematics with Aications, 43(3), 473-490, 2002. Google Scholar

[108] Lendasse A., Lee J., Bodt E., Wertz V., Verleysen M., Aoximation by Radial Basis Function Networks-Aication to Option Pricing. Connectionist Aoaches in Economics andManagement Sciences, 201-212, 2003. Google Scholar

[109] Chen T., Chen H., Aoximation Capability to Functions of Several Variables, Nonlinear Functional and Operators by Radial Basis Function Neural Networks. IEEE Trans Neural Networks, 6(4), 904-910, 1995. Google Scholar

[110] Dyn N., Ron A., Radial Basis Function Aoximation: from Gridded Centers to Scattered Centers. Proc London Math Soc, 71, 76-108, 1995. Google Scholar

[111] Ron A., The L2-aoximation Orders of Principal Shift-Invariant Spaces Generated by a Radial Basis function. In: D. Braess, L.L. Schumaker (Eds.), Numerical Methods in Aoximation Theory, Verilag, Basel, 9, 245-268, 1992. Google Scholar

[112] Fasshauer G.E., Solving Differential Equations with Radial Basis Functions: Multilevel Methods and Smoothing. Adv. Comp. Math., 11, 139-159, 1999c. Google Scholar

[113] Li X., Micchelli C.A., Aoximation by Radial Bases and Neural Networks. Numerical Algorithms, 25(1), 241-262, 2000. CrossrefGoogle Scholar

[114] Golberg M.A., Chen C.S., Bowman H., Power H., Some Comments on the Use of Radial Basis Functions in the Dual Reciprocity Method. Computational Mechanics, 21(2), 141-148, 1998. CrossrefGoogle Scholar

[115] Golberg M.A, Chen C.S., Bowman H., Some Recent Results and Proposals for the use of Radial Basis Functions in the BEM. Engineering Analysis with Boundary Elements, 23(4), 285-296, 1999. Google Scholar

[116] Golberg M.A., Chen C.S., The Theory of Radial Basis Functions Aied to the BEM for Inhomogeneous Partial Differential Equations. Boundary Elements Comm., 5, 57-61, 1994. Google Scholar

[117] Karur S.R., Ramachandran P.A., Radial Basis Function Aoximation in the Dual Reciprocity Method. Math. Comput. Model, 20, 59-70, 1994. CrossrefGoogle Scholar

[118] Hua Z., Wei Z., Yaowu C., A No-reference Perceptual Blur Metric by Using OLS-RBF Network. In Proceedings of Pacific-Asia Workshop on Computational Intelligence and Industrial Aication (PACIIA’08), 1, 1007-1011, 2008. Google Scholar

[119] Iske A., Reconstruction of Smooth Signals from Irregular Samples by using Radial Basis Function Aoximation. In Proceeding of International Workshop on Sampling Theory and Aications, 82-87, 1999. Google Scholar

[120] Kluk P., Misiurski G., Morawski R.Z., Total Least Squares Vs. RBF Neural Networks in Static Calibration of Transducers. In Proceedings of Instrumentation and Measurement Technology Conference (IMTC/97), 424-427, 1997. Google Scholar

[121] Schaback R. (1993). Comparison of Radial Basis Function Interpolants in Multivariate Aoximation: From CAGD to Wavelets. In: K. Jetter, F. Utreras (Eds.), World Scientific Publishing, Singapore, 1993, 293-305. Google Scholar

[122] Zhou G.,Wang C., SuW., Nonlinear Output Regulation Based on RBF Neural Network Aoximation. In Proceedings of International Conference on Control and Automation (ICCA’05), 2, 679-684, 2005. Google Scholar

[123] Faul A.C., Powell M.J.D., Krylov Subspace Methods for Radial Basis Function Interpolation. Numerical Analysis, Chapman & Hall/CRC, Boca Raton, FL, 200, 115-141. Google Scholar

[124] Foley T.A., Dayanand S., Zeckzer D., Localized Radial Basis Methods using Rational Triangle Patches in Geometric Modeling. Computing Suement, 10, 163-176, 1995. Google Scholar

[125] Lai S.J., Wang B.Z., Duan Y., Eigen Value Analysis of Spherical Resonant Cavity Using Radial Basis Functions, Progress in Electromagnetic Research Letters, 24, 69-76, 2011. Google Scholar

[126] Hon Y.C., Schaback R., On UnSymmetric Collocation by Radial Basis Functions. A Math Comput., 119, 177-186, 2001.Google Scholar

[127] Wu Z., On the Convergence of the Interpolation with Radial Basis Function. Chinese Ann. Math. Ser. A, 14, 480-486, 1993. Google Scholar

[128] Jackson I.R.H., Convergence Properties of Radial Basis Functions. Constr Aox, 4, 243-264, 1988. Google Scholar

[129] Wendland H., Numerical Solution of Variational Problems by Radial Basis Functions. Aoximation Theory IX, 2, 361-368, 1999. Google Scholar

[130] Wendland H., Meshless Galerkin Methods using Radial Basis Functions. Math. Comp., 68, 1521-1531, 1999. Google Scholar

[131] Beatson R.K., Light W.A., Fast Evaluation of Radial Basis Functions: Methods for two-dimensional polyharmonic splines. IMA Journal Numerical Analysia, 17(3), 343-372, 1997. Google Scholar

[132] Beatson R.K, Light W.A., Billings S., Fast Solution of the Radial Basis Function Interpolation Equations: Domain Decomposition Methods. SIAM J. Sci. Comput, 22(5), 1717-1740, 2000. Google Scholar

[133] Oliveira A., Medeiros E., Rocha T., Bezerra M., Veras R., A Study on the Influence of Parameter on Performance of RBF Neural Networks Trainedwith the Dynamic Decay Adjustment Algorithm. In Proceedings of Fifth International Conference on Hybrid Intelligent Systems (HIS’05), 512-514, 2005. Google Scholar

[134] Ri S., An Algorithm for Selecting a Good Value for the Parameter c in Radial Basis Function Interpolation. Adv. Comput.Math., 11, 193-210, 1999. Google Scholar

[135] Schwenker F., Dietrich C., Initialization of Radial Basis Function networks by Means of Classification Trees. Neural Network World, 10 473-482, 2000. Google Scholar

[136] Kubat M., Decision Trees can Initialize Radial Basis Function Networks. IEEE Transactions on Neural Networks, 9, 813-821, 1998. Google Scholar

[137] Karri V., Frost F., Effect of Altering the Gaussian Function Receptive Field Width in RBF Neural Networks on Aluminum Fluoride Prediction in Industrial Reduction Cells. In Proceedings of 6th International Conference on Neural Information Processing (ICONIP’99), 1, 101-106, 1999. Google Scholar

[138] Musavi M.T., Ahmed W., Chan K.H., Faris K.B., Hummels D.M., On the Training of Radial Basis Function Classifiers. Neural Networks, 5(4), 595-603, 1992. CrossrefGoogle Scholar

[139] Oyang Y., Hwang S., Ou Y., Chen C., Chen Z., Data Classification with Radial Basis Function Networks Based on a novel Kernel Density Estimation Algorithm. IEEE Transactions on Neural Networks, 16(1), 225-236, 2005. Google Scholar

[140] Bouamar M., Ladjal M., A Comparative Study of RBF Neural Network and SVM Classification Techniques Performed on Real Data for Drinking Water Quality. Systems Signals and Devices. In Proceedings of 5th IEEE International Multi-Conference on SSD, 1-5, 2008. Google Scholar

[141] Guezouri M., Mesbahi L., A New Aoach Using Temporal Radial Basis Function in Chronological Series. Int. Arab J. Inf. Technol., 6(1), 85-90, 2009. Google Scholar

[142] Wang D. et al., Extraction and Optimization of Fuzzy Protein Sequences Classification Rules using GRBF Neural Networks. Neural Information Processing-Letters and Reviews, 1(1), 53-57, 2003. Google Scholar

[143] Bruzzone L., Prieto D.F., A Technique for the Selection of Kernel-Function Parameters in RBF Neural Networks for Classi- fication of Remote-Sensing Images. IEEE Transactions on Geoscience and Remote Sensing, 37(2), 1179-1184, 1999. Google Scholar

[144] Krzyzak A., Linder T., Lugosi C., Nonparametric Estimation and Classification using Radial Basis Function Nets and Empirical Risk Minimization. IEEE Trans. Neural Networks, 7(2), 475-487, 1996. CrossrefGoogle Scholar

[145] Chang C.Y., Fu S.Y., Image Classification using a Module RBF Neural Network. In Proceedings of the 1st International Conference on Innovation Computing, Information and Control (ICICIC’ 06), 2, 270-273, 2006. Google Scholar

[146] Daqi G., Genxing Y., Adaptive RBF Neural Networks for Pattern Classifications. In Proceedings of Int. Joint Con. on Neural Network (IJCNN 2002), 1, 846-85, 2002. Google Scholar

[147] Dybowski R., Classification of Incomplete Feature Vectors by Radial Basis Function Networks. Pattern Recognition Letters, 19(14), 1257-1264, 1998. CrossrefGoogle Scholar

[148] El Zooghby A., Christodoulou C., Georgiopoulos M., Performance of Radial Basis Function Networks for Direction of Arrival Estimation with Antenna Arrays. IEEE Trans. Antennas and Propagation, 45(11), 1611-1617, 1997. CrossrefGoogle Scholar

[149] Peng J., Li K., Huang D., A Hybrid Forward Algorithm for RBF Neural Network Construction. IEEE Transactions on Neural Networks, 17(6), 1439-1451, 2006. Google Scholar

[150] Suresh S., Babu R.V., Kim H.J., No-reference image quality assessment using modified extreme learning machine classifier. A. Soft Comput., 9(2), 541–552, 2009. CrossrefGoogle Scholar

[151] Fu X.,Wang L., A GA-Based RBF Classifierwith Class-Dependent Features.In the Proceedings of the 2002 Congress on Evolutionary Computation, 2002. CEC’02, 2, 18890-1894, 2002. Google Scholar

[152] Fu X.,Wang L., Chua K., Chu F., Training RBF Neural Networks on Unbalanced Data. In Proceedings of the 9th International Conference on Neural Information Processing (ICONIP’02), 1016- 1020, 2002. Google Scholar

[153] Peng H., Ozaki T., Haggan-Ozaki V., Toyoda Y., A Parameter Optimization Method for Radial Basis Function Type Models. IEEE Transactions on Neural Networks, 14(2), 432-438, 2003. Google Scholar

[154] Dash C.S.K., Behera A.K., Dehuri S., Cho Sung-Bae, Differential Evolution-Based Optimization of Kernel Parameters in Radial Basis Function Networks for Classification. Int. Journal of Aied Evolutionary Computation (IJAEC), 4(1), 56-80, 2013. Google Scholar

[155] Dash C.S.K., Behera A.K., Pandia M.K., Dehuri S., Neural Networks Training Based on Differential Evolution in Radial Basis Function Networks for Classification of Web Logs. In Proceedings of International Conference on Distributed Computing and Internet Technology, 183-194, 2013. Google Scholar

[156] Buhmann M.D., Pre-Wavelet on Scattered Knots and from Radial Function Spaces: A Review. In: Martin R.R. (Ed.), 6th IMA Conference on the Mathematics of Surfaces, Clarendon Press, Oxford, 1995, 309-324. Google Scholar

[157] Dash C.S.K., Dash A.P., Dehuri S., Cho S.-B., Wang G.-N., DE+RBFNs Based Classification: A Special Attention to Removal of Inconsistency and Irrelevant Features. Engineering Aications of AI, 26(10), 2315-2326, 2013. Google Scholar

[158] Nagabhushan T.N., Ko H., Park J., Padma S.K., Nijagunarya Y.S., Classification of Symbolic Objects Using Adaptive Auto- Configuring RBF Neural Networks. In Proceedings of International Symposium on Information Technology Convergence (ISITC 2007), 22-26, 2007. Google Scholar

[159] Pulido A. et al., Radial Basis Functions aied to the classification of UV-visible spectra1. Analytica Chimica Acta, 388(3), 273-281, 1999. Google Scholar

[160] Qin Z., Chen J., Liu Y., Lu J., Evolving RBF Neural Networks for Pattern Classification. Computational Intelligence and Security, LNAI 3081, 957-964, 2005. Google Scholar

[161] Baragada S.R., Ramakrishna S., Rao M.S., Purushothaman S., Polynomial Discriminant Radial Basis Function for Steganalysis. IJCSNS, 9(2), 209-218, 2009. Google Scholar

[162] Kurban T., Besdok E., A Comparison of RBF Neural Network Training Algorithms for Inertial Sensor Based Terrain Classifi- cation. Sensors, 9, 6312-6329, 2009. CrossrefGoogle Scholar

[163] Pan X., Lu Y., Cao Y., Zhang H., Zhao Y., Xu X., Research on the Algorithm of Noisy-Robust Tibetan Speech Recognition Based on RBF. In Proceedings of International Symposium on Intelligent Information Technology Aication Workshops (IITAW’08), 416-419, 2008. Google Scholar

[164] Niranjan M., Fallside F., Neural Network and Radial Basis Functions in Classifying Static Speech Patterns. Computer Speech and Language, 4(3), 275-289, 1990. CrossrefGoogle Scholar

[165] Shan B., License Plate Character Segmentation and Recognition Based on RBF Neural Network. In Proceedings of 2nd International Workshop on Education Technology and Computer Science (ETCS), 2, 86-89, 2010. Google Scholar

[166] Sitamahalakshmi T., Babu D., Jagadeesh M., Mouli K., Performance Comparison of Radial Basis Function Networks and Probabilistic Neural Networks for Telugu Character Recognition. Global Journal of Computer Science and Technology, 11(4), 9-15, 2011. Google Scholar

[167] Lee Y., Hand Written Digit Recognition Using K-Nearest Neighbor, Radial Basis Function and Back propagation Neural Networks. Neural Computation, 3, 440-449, 1991. CrossrefGoogle Scholar

[168] Hwang Y.S., Bang S.Y., An Efficient Method to Construct a Radial Basis Function Classifier Neural Networks. Neural Networks, 10(8), 1495-1503, 1997. CrossrefGoogle Scholar

[169] Baboo S.S., Subashini P., Krishnaveni M., Combining Self- Organizing Maps and Radial Basis Function Networks for Tamil Handwritten Character Recognition. ICGST International Journal on Graphics, Vision, and Image Processing, 9, 49-56, 2009. Google Scholar

[170] Li Q., Tufts D.W., Principal Feature Classification. IEEE Transactions Neural Networks, 8(1), 155-160, 1997. Google Scholar

[171] Subashini T.S., Ramalingam V., Palanivel S., Breast Mass Classi fication Based on Cytological Patterns using RBFNN and SVM. Expert Systems with Aications, 36, 5284-5290, 2008. Google Scholar

[172] Chu F., Wang L., Aying RBF Neural Networks to Cancer Classifi- cation Based on Gene Expressions. In Proceedings of Int. Joint Conference on Neural Networks (IJCNN’06), 1930-1934, 2006. Google Scholar

[173] Kumar M., Srinivas D.S., Unsupervised Image Classification by Radial Basis Function Neural Network. In Proceedings of 22nd Asian Conference on Remote Sensing, 5, 9, 2001. Google Scholar

[174] Thakur S., Sing J.K., Basu D.K., Nasipuri M., Kundu M., Face Recognition using Principal Component Analysis and RBF Neural Networks. In Proceedings of First International Conference on Emerging Trends in Engineering and Technology (ICETET’08), 695-700, 2008. Google Scholar

[175] Dhanalakshmi P., Palanivel S., Ramalingam V., Classification of Audio Signals Using SVMand RBFNN. Expert Systems with Aications, 36(3), 6069-6075, 2009. Google Scholar

[176] RosenblumM., Yacoob Y., Davis L.S., Human Expression Recognition from Motion using a Radial Basis Function Network Architecture. IEEE Trans. Neural Networks, 7, 1121-1138, 1996. CrossrefGoogle Scholar

[177] Arad N., Dyn N., Reisfeld D., Yeshurun Y., Image Warping by Radial Basis Functions: Aications to Facial Expressions. CVGIP: Graphical Models and Image Processing, 56(2), 161-172, 1994. Google Scholar

[178] Fanhui X., Lixin S., Zhan Y., A Container Handling Capacity Prediction Model Based on RBF Neural Networks and its Simulation. In Proceedings of IEEE International Conference on Control and Automation (ICCA’2007), 1098-1100, 2007. Google Scholar

[179] Leonard J.A., Kramer M.A., Ungar J.H., Using Radial Basis Functions to Aoximate a Function and its Error Bounds. IEEE Trans. on Neural Networks, 3(4), 624-627, 1992. Google Scholar

[180] Mai-Duy N., Tran-Cong T., Numerical Solution of Differential Equations Using Multiquadric Radial Basis Function Networks. Neural Networks, 14(2), 185-199, 2001. Google Scholar

[181] Ling L., Kansa E.J., A Least-Squares Preconditioner for Radial Basis Functions Collocation Methods. Advances in Computational Mathematics, 23(1-2), 31-54, 2005. Google Scholar

[182] Schaback R., Wendland H., Using Compactly Surted Radial Basis Functions to Solve Partial Differential Equations. Boundary Element Technology, 13, 311-324, 1999. Google Scholar

[183] Fasshauer G.E., On Smoothing for Multilevel Aoximation with Radial Basis Functions. Aoximation Theory IX, 2, 55-62, 1999a. Google Scholar

[184] Fasshauer G.E., On the Numerical Solution of Differential Equations with Radial Basis Functions. Boundary Element Technology, 13, 291-300, 1999b. Google Scholar

[185] Power H., Barraco V., A Comparison Analysis between Unsymmetric and Symmetric Radial Basis Function Collocation Methods for the Numerical Solution of Partial Differential Equations. Computers and Mathematics with Aications, 43(3-5), 551-583, 2002. Google Scholar

[186] Schaback R., Wendland H., Using Compactly Surted Radial Basis Functions to Solve Partial Differential Equations. Boundary Element Technology, 13, 311-324, 1999. Google Scholar

[187] Jianyu L., Siwei L., Yingjian Q., Yaping H., Numerical Solution of Elliptic Partial Differential Equation using Radial Basis Function Neural Networks. Neural Networks, 16(5), 729-734, 2003. CrossrefGoogle Scholar

[188] Wetterschereck D., Dietterich T., Improving the Performance of Radial Basis Function Networks by Learning Center Locations. Advances in Neural Information Processing Systems, 4, 1133- 1140, 1992. Google Scholar

[189] Chun-tao M., Kun W., Li-yong Z., A New Training Algorithm for RBF Neural Network Based on PSO and Simulation Study. In Proceedings of WRI World Congress on Computer Science Info. Engineering, 4, 641-645, 2009. Google Scholar

[190] Senapati M.R., Vijaya I., Dash P.K., Rule Extraction from Radial Basis Functional Neural Networks by using Particle Swarm Optimization. Journal of Computer Science, 3(8), 592-599, 2007. Google Scholar

[191] Billings S.A., Zheng G.L., Radial Basis Function Network Configuration using Genetic Algorithms. Neural Networks, 8(6), 877- 890, 1995. CrossrefGoogle Scholar

[192] Yuan J.L., Li X.Y., Zhong L., Optimized Grey RBF Prediction Model Based on Genetic Algorithm. In Proceedings of International Conference on Computer Science and Software Engineering, 1, 74-77, 2008. Google Scholar

[193] Yu B., He X., Training Radial Basis Function Networks with Differential Evolution. In Proceedings of World Academy of Science Engineering and Technology, 11, 157-160, 2006. Google Scholar

[194] Shen Y., Bu Y., Yuan M., Study on Weigh-in-Motion System Based on Chaos Immune Algorithm and RBF Network. In Proceedings of Pacific-Asia Workshop on Computational Intelligence and Industrial Aication (PACIIA’08), 2, 502-506, 2008. Google Scholar

[195] YunnaW., Zhaomin S., Aication of RBF Neural Network Based on Ant Colony Algorithm in Credit Risk Evaluation of Construction Enterprises. In Proceedings of International Conference on Risk Management & Engineering Management (ICRMEM’08), 653- 658, 2008. Google Scholar

[196] Zhao Z.Q., Huang D.S., A Mended Hybrid Learning Algorithm for Radial Basis Function Neural Networks to Improve Generalization Capability. Aied Mathematical Modeling, 31, 1271-1281, 2007. Google Scholar

[197] Chen S., Cowan C.F.N., Grant P.M., Orthogonal Least Squares Learning Algorithm Basis Function Networks. IEEE Trans. Neural Networks, 2(2), 302-309, 1991. CrossrefGoogle Scholar

[198] Liu Y., Zheng Q., Shi Z., Chen J., Training Radial Basis Function Networks with Particle Swarms. Advances in Neural Networks– ISNN, 3174, 317-322, 2004. Google Scholar

[199] Fatemi M., Roopaei M., Shabaninia F., New Training Methods for RBF Neural Networks. In Proceedings of International Conference on Neural Networks and Brain (ICNN&B’05), 3, 1322-1327, 2005. Google Scholar

[200] Tan S., Hao J., Vandewalle J., Nonlinear Systems Identification using RBF Neural Networks. In Proceedings of International Joint Conference on Neural Networks (IJCNN’93), 2, 1833-1836, 1993. Google Scholar

[201] De Lacerda E., de Carvalho A., Ludermir T., Model Selection via Genetic Algorithms for RBF Networks. Journal of Intelligent and Fuzzy Systems, 13, 111-122, 2003. Google Scholar

[202] Kondo N., Hatanaka T., Uosaki K., Pattern Classification by Evolutionary RBF Networks Ensemble Based onMulti-Objective Optimization. In Proceedings of International Joint Conference on Neural Networks, Korea, 2919-2925, 2006. Google Scholar

[203] Kondo N., Hatanaka T., Uosaki K., Non-linear Dynamic System Identification based on Multi-objectively Selected RBF Networks. In Proceedings of the IEEE Symposiumon Computational Intelligence in Multi-criteria Decision Making, 112–127, 2007. Google Scholar

[204] Platt J.C., A Resource Allocation Network for Function Interpolation. Neural Comput., 3(2), 213–225, 1991. CrossrefGoogle Scholar

[205] Lampariello F., Sciandrone M., Efficient Training of RBF Neural Networks for Pattern Recognition. IEEE Transactions on Neural Networks, 12(5), 1235-1242, 2001. Google Scholar

[206] Barra T.V., Bezerra G.B., de Castro L.N., Von Zuben F.J., An Immunological Density-Preserving Aoach to the Synthesis of RBF Neural Networks for Classification. In Proceedings of International Joint Conference on Neural Networks (IJCNN’06), 929-935, 2006. Google Scholar

[207] Ahmad R., Jamaluddin H., Radial Basis Function (RBF) for Non- Linear Dynamic System Identification. Journal Teknologi, 36, 39- 54, 2002. Google Scholar

[208] Onsivilai A., Saichoomdee S., Distance Transmission Line Protection Based on Radial Basis Function Network. World Academy of Science, Engineering and Technology, 60, 81-84, 2009. Google Scholar

[209] Bi G., Dong F., A Sequential Learning Algorithm for RBF Networks and its Aication to Ship Course-Changing Control. In Proceedings of Control and Decision Conference (CCDC), Chinese, 3779-3784, 2008. Google Scholar

[210] Bing H., Gang L., Cun G., Jiang G., Modulation Recognition of Communication Signal Based on Wavelet RBF Neural Network. In Proceedings of 2nd International Conference on Computer Engineering and Technology (ICCET), 2, V2-490, 2010. Google Scholar

[211] Blu T., Unser M.,Wavelets, Fractals, and Radial Basis Functions. IEEE Transactions on Signal Processing, 50(3), 543-553, 2002. Google Scholar

[212] Cha I., Kassam S.A., Interference Cancellation using Radial Basis Function Networks. EURASIP, Signal Processing, 47(3), 247- 268, 1995. Google Scholar

[213] Cid-Sueira J., Artes-Rodriguez A., Figueiras-Vidal A.R., Recurrent Radial Basis Function Networks for Optimal Symbol-by- Symbol Equalization. EURASIP Signal Processing, 40, 53-63, 1994. Google Scholar

[214] Dhanalakshmi P., Palanivel S., Ramalingam V., Classification of Audio Signals using SVMand RBFNN. Expert Systems with Aications, 36, 6069-6075, 2008. Google Scholar

[215] Cui H., Research of Optimizing Ignition Control System in Gaseous Fuel Engine Based on RBF Neural Network. In Proceedings of International Conference on Intelligent Computation Technology and Automation (ICICTA), 1, 399-403, 2008. Google Scholar

[216] Dai J., Liu X., Zhang S., Zhang H., Yu Y., Zheng X., Neuronal Spike Sorting Based on 2-stage RBF Networks. In Proceedings of 2nd Int. Con. on Future Generation Communication and Networking (FGCN 2008), 3, 47-50, 2008. Google Scholar

[217] Dash P.K., Mishra S., Panda G., A Radial Basis Function Neural Network Controller for UPFC. IEEE Trans Power Syst, 15, 1293- 1299, 2000. CrossrefGoogle Scholar

[218] Ding J.,Wang L., Forecast of RBF Neural Networks to Weak Electrical Signals in Plant. In Proceedings of International Conference on Artificial Intelligence and Computational Intelligence, 1, 621-625, 2009. Google Scholar

[219] Ding S., JiaW., Su C., Chen J., Research of Neural Network Algorithm Based on FA and RBF. In Proceedings of 2nd International Conference on Computer Engineering and Technology (ICCET), 7, V7—228, 2010. Google Scholar

[220] Dongli Y., Jianguo Y., Qingbiao X., A Study of Fault Detection and System Reconfiguration for UAV Navigation System bon RBF Neural Network. In Proceedings of 7th World Congress on Intelligent Control and Automation (WCICA 2008), 55-58, 2008. Google Scholar

[221] Esmaeilbeigi M., Hosseini M., Mohyud-Din S., A New Aoach of the Radial Basis Functions Method for Telegraph Equations. Int J Physical Sciences, 6(6), 1517-1527, 2011. Google Scholar

[222] Fan G., Dai Y., Wang H., Gaussian Sum Particle Filtering Based on RBF Neural Networks. In Proceedings of 7th World Congress on Intelligent Control and Automation (WCICA’2008), 3071- 3076, 2008. Google Scholar

[223] Fraile R., Rubio L., Cardona N., Aication of RBF Neural Networks to the Prediction of Propagation Loss over Irregular Terrain. In Proceedings of 52nd IEEE VTS-Fall VTC Conference, 2, 878-884, 2000. Google Scholar

[224] Girosi F., Some Extensions of Radial Basis Functions and their Aications in Artificial Intelligence. Comput. Math. A., 24, 61-80, 1992. Google Scholar

[225] Gao L., Wang R., Yang S., Chai Y., An Image Fusion Algorithm Based on RBF Neural Networks. In Proceedings of International Conference onMachine Learning and Cybernetics, 8, 5194-5199, 2005. Google Scholar

[226] Ghosh J., Nag A., Knowledge Enhancement and Reuse with Radial Basis Function Networks. In Proceedings of the 2002 International Joint Conference on Neural Networks (IJCNN’02), 2, 1322-1327, 2002. Google Scholar

[227] Guanglan Z., Abeyratne U.R., Saratchandran P., Comparing RBF and BP Neural Networks in Dipole Localization. In Proceedings of First Joint BMES/EMBS Conference, 2, 939, 1999. Google Scholar

[228] Guo J., Gong J., Xu J., Improved RBF Neural Network for Nonlinear Identification System. In Proceedings of the 2009 International Workshop on Information Security and Aication, 137-140, 2009. Google Scholar

[229] Chen S., Billings S.A., Grant P.M., Recursive Hybrid Algorithm for Nonlinear System Identification using Radial Basis Function Networks. Int. J. Control, 55(5), 1051-1070, 1992. CrossrefGoogle Scholar

[230] HaoW., Li X., Zhang M., Aication of RBF Neural Network to Temperature Compensation of Gas Sensor. In Proceedings of 2008 International Conference of Computer Science and Software Engineering, 4, 839-842, 2008. Google Scholar

[231] Hon Y.C., Mao X.Z., A Radial Basis Function Method for Solving Options Pricing Model. Journal of Financial Engineering, 8, 31- 49, 1999. Google Scholar

[232] Jayawardena A.W., Fernando D.A.K., Use of Radial Basis Function Type Artificial Neural Networks for Runoff Simulation. Computer-Aided Civil and Infrastructure Engineering, 13, 91-99, 1997. Google Scholar

[233] Jianwei Z., Yu Z., Yina Z., Longfei Z., Damage Diagnosis of RadialGate Based on RBF Neural networks. In Proceedings of 2009 International Forumon Computer Science-Technology and Aications, 3, 399-402, 2009. Google Scholar

[234] McGarry K.J., Wermter S., MacIntyre J., Knowledge Extraction from Radial Basis Function Networks and Multilayer Perceptrons. In Proceedings of International Joint Conference on Neural Networks (IJCNN’99), 4, 2494-2497, 1999. Google Scholar

[235] KeKang L.L., Zhang S., Research and Aication of Compound Control Based on RBF Neural Network and PID. In Proceedings of 3rd International Conference on Intelligent System and Knowledge Engineering (ISKE 2008), 1, 848-850, 2008. Google Scholar

[236] Krzyzak A., Linder T., Radial Basis Function Networks and Complexity Regularization in Function Learning. IEEE Trans. Neural Networks, 9, 247-256, 1997. Google Scholar

[237] Lai S., Wang B., Duan Y., Aication of the RBF-based Meshless Method to Solve 2-D Time Domain Maxwell’s Equations. In Proceedings of International Conference on Microwave and Millimeter Wave Technology (ICMMT 2008), 2, 749-751, 2008. Google Scholar

[238] Lin G.F., Chen L., A Non-Linear Rainfall-Runoff Model using Radial Basis Function Network”, Journal of Hydrology, 289, 1-8, 2004. Google Scholar

[239] Liu H., Gong Z., Li M., Sliding Mode Control of ROV Based on RBF Neural Networks Adaptive Learning. In Proceedings of 3rd International Conference on Intelligent System and Knowledge Engineering (ISKE’08), 1, 590-594, 2008. Google Scholar

[240] Ma L., Xin K., Liu S., Using Radial Basis Function Neural Networks to CalibrateWater Quality Model. International Journal of Intelligent Systems and Technologies, 3(2), 90-98, 2008. Google Scholar

[241] Liu S., Yu Q., Lin W., Yang S.X., Tracking Control of Mobile Robots Based on Improved RBF Neural Networks. In Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, 1879-1884, 2006. Google Scholar

[242] El Shafie A., El-Shafie A., Almukhtar A., Taha M., ElMazoghi H., Shehata A., Radial Basis Function Neural Networks for Reliably Forecasting Rainfall. Journal ofWater and Climate Change, 3(2), 125-138, 2012. Google Scholar

[243] Neruda R., Vidnerov P., Learning Errors by Radial Basis Function Neural Networks and Regularization Networks. International Journal of Grid and Distributed Computing, 1(2), 49-57, 2008. Google Scholar

[244] Yang X., Dai H., Sun Y., A Hybrid Modeling Method Based on Mechanism Analysis, Identification, and RBF Neural Networks. In Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, 2, 1310-1315, 2008. Google Scholar

[245] Yee MS., Yeap B.L., Hanzo L., Radial Basis Function-Assisted Turbo Equalization. IEEE Transactions on Communications, 51, 664-675, 2003. Google Scholar

[246] Rodriguez N., Yaez E., Crawford B.,Wavelet Based Autoregressive RBF Network for Sardines Catches Forecasting. In Proceedings of Third International Conference on Convergence and Hybrid Information Technology (ICCIT’08), 2, 808-811, 2008. Google Scholar

[247] Sadr A., Mohesenifar N., Okhovat R.S., Comparison of MLP and RBF neural networks for Prediction of ECG Signals. IJCNS Int J Comput Sci Network Security, 11(11), 124-128, 2011. Google Scholar

[248] Arnott R., (1993). An Adaptive Radial Basis Function Diversity Combiner. IEEE Electronics Letters, 29(12), 1092-1094, 1993. CrossrefGoogle Scholar

[249] Chen S., Gibson G.J., Cowan C.F.N., Grant P.M., Reconstruction of Binary Signals using an Adaptive Radial-Basis-Function Equalizer. EURASIP Signal Processing, 22, 77-93, 1991. Google Scholar

[250] Baxter B.J.C., (2002). Preconditioned Conjugate Gradients, Radial Basis Functions, and Toeplitz Matrices. Computers & Math with Aications, 43(3), 305-318, 2002. Google Scholar

[251] Bezerra M.E.R., Oliveira A.L.I., Meira S.R., A Constructive RBF Neural Network for Estimating the Probability of Defects in Software Modules. In Proceedings of International Joint Conference on Neural Networks (IJCNN 2007), 2869-2874, 2007. Google Scholar

[252] Chakravarthy S.V., Ghosh J., Scale-Based Clustering using the Radial Basis Function Network. IEEE Trans. Neural Networks, 7(5), 1250-1261, 1996. CrossrefGoogle Scholar

[253] Chen S., McLaughlin S., Mulgrew B., Complex-valued Radial Basis Function Network, Part I: Network Architecture and Learning Algorithms. Signal Processing, 35(1), 19-31, 1994. CrossrefGoogle Scholar

[254] Gomes J., Barroso V., Using an RBF Network for Blind Equalization: Design and Performance Evaluation. IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP-97, 4, 3285-3288, 1997. Google Scholar

[255] Chang E.S., Chen S., Mulgrew B., Gradient radial basis function networks for nonlinear and nonstationary time series prediction. IEEE Trans. Neural Networks, 7(1), 190-194, 1996. CrossrefGoogle Scholar

[256] Ferreira A., Joaquim M., da Cunha Roque C., Carrera E., Cinefra M., Polit O., Analysis of Sandwich Plates by Radial Basis Functions Collocation, According toMurakami’s Zig-Zag Theory. Journal of Sandwich Structures and Materials, 14(5), 505-524, 2012. Google Scholar

[257] Kattan A., Galvan E., Evolving Radial Basis Function Networks via GP for Estimating Fitness Values using Surrogate Models. In Proceedings of IEEE Congress on Evolutionary Computation (CEC’2012), 1-7, 2012. Google Scholar

[258] Pottmann M., Peter Jorgl H., Radial Basis Function Networks for Internal Model Control. AiedMathematics and Computation, 70(2-3), 283-298, 1995. Google Scholar

[259] Sadeghkhani I., Ketabi A., Feuillet R., Radial Basis Function Neural Network Aication to Power System Restoration Studies. Computational Intelligence and Neuroscience, 2012, 654895, 2012. Google Scholar

[260] Xu L., Kryzak A., Yuille A., On Radial Basis Function Nets and Kernel Regression Statistical Consistency, Convergence Rates, and Receptive Field Sizes. Neural Networks, 7, 609-628, 1994. CrossrefGoogle Scholar

[261] Bos F.M., Van Oudheusden B.W., Bijl H., Radial Basis Function Based Mesh Deformation Aied to Simulation of FlowAround Flang Wings. Computers & Fluids, 79, 167-177, 2013. Google Scholar

[262] Dybowski R., Classification of Incomplete Feature Vectors by Radial Basis Function Networks. Pattern Recognition Letters, 19, 1257-1264, 1998. CrossrefGoogle Scholar

[263] Nagabhushan T.N., Padma S.K., On the Generalization of Incremental Learning RBF Neural Networks Trained with Significant Patterns. In Proceedings of 6th International Conference on Information, Communications, & Signal Processing, 2, 1-5, 2007. Google Scholar

[264] Ding Y., Zhao J., Yuan Z., Zhang Y., Long C., Xiong L., Constrained Surface Recovery using RBF and its Efficiency Improvements. Journal of Multimedia, 5(1), 55-66, 2010. Google Scholar

[265] Er M.J., ChenW.,Wu S., High-speed Face Recognition Based on Discrete Cosine Transform and RBF Neural networks. IEEE Trans Neural Networks, 16, 679-691, 2005. Google Scholar

[266] Er M., Wu S., Lu J., Toh H., Face Recognition with Radial Basis Function (RBF) Neural Networks. IEEE Transactions on Neural Network, 33, 697-710, 2002. Google Scholar

[267] Gan Q., Saratchandran P., Sundararajan N., Subramanian K.R., A Complex Valued Radial Basis Function Network for Equalization of Fast Time Varying Channels. IEEE Transactions on Neural Networks, 10(4), 958-960, 1999. Google Scholar

[268] Kantsila A., Lehtokangas M., Saarinen J., On Radial Basis Function Network Equalization in the GSM System. In Proceedings of European Symposium on Artificial Neural Networks, Bruges, 179-184, 2003. Google Scholar

[269] Liu Y., Zhang X., Hua J., Fault Diagnosis Based on Radial Basis Function Neural Network in Particleboard Glue Mixing and Dosing System. In Proceedings of Control and Decision Conference (CCDC 2008), 774-778, 2008. Google Scholar

[270] Cid-Sueiro J., Figueiras-Vidal A.R., Recurrent Radial Basis Function Networks for Optimal Blind Equalisation. In Proceedings of the 1993 IEEE-SP Workshop on Neural Networks for Signal Processing, 562-571, 1993. Google Scholar