[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
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