Intelligent Initialization of Resource Allocating RBF Networks

[journal]

Full reference

M. Wallace, N. Tsapatsoulis, S. Kollias, Intelligent Initialization of Resource Allocating RBF Networks, Neural Networks 18 (2), pp. 117-122, 2005

27 known citations

1. S. D'Alton, A Constructive Neural Network Incorporating Competitive Learning of Locally Tuned Hidden Neuronsd Thesis, University of Tasmania, January 2006
2. K. Du and M.N. Swamy, Neural Networks in a Softcomputing Framework, Springer-Verlag, New York, 2006
3. W. Pedrycz, H.S. Park and S.K. Oh, A granular-oriented development of functional radial basis function neural networks, Neurocomputing, vol 72, pp 420-4345, 2008
4. H. Tamura and K. Tanno, Midpoint-Validation Method of Neural Networks for Pattern Classification Problems, In Proceedings of the Second international Conference on Innovative Computing, Information and Control, 2007
5. M.D. Perez-Godoy, A.J. Rivera Rivas, M.J. del Jesus and I. Rojas, CoEvRBFN: An Approach to Solving the Classification Problem with a Hybrid Cooperative-Coevolutive Algorithm, Proceedings of the 9th International Work-Conference on Artificial Neural Networks, IWANN 2007, San Sebastian, Spain, 2007
6. M.G.C.A. Cimino , W. Pedrycz, B. Lazzerini and F. Marcelloni, Using multilayer perceptrons as receptive fields in the design of neural networks, Neurocomputing, vol 72(10-12), pp. 2536-2548, 2009
7. H. Tamura and K. Tanno, Midpoint-Validation Method for Support Vector Machine Classification, IEICE Transactions on Information and Systems, vol 7, pp. 2095-2098, 2008
8. M.D. Perez, A.J. Rivera Rivas, M.J. del Jesus, I. Rojas. Optimizacion de coevrbf para aumentar su eficiencia en tareas de clasificacion, Simposio De Inteligencia Computacional, Zaragoza, Septiembre 2007
9. R. Eickhoff, Fehlertolerante neuronale Netze zur Approximation von Funktionen, Universitat Paderborn, Germany, PhD Thesis, 2007
10. F. Xue, L. Ge and B. Wang, Pipelined Genetic Algorithm Initialized RAN Based RBF Modulation Classifier, In Proceedings of the 6th international Symposium on Neural Networks: Advances in Neural Networks - Part II, Wuhan, China, May 26-29, 2009
11. H.S. Park and S.K. Oh, The Design of granular-based radial basis function neural network by context-based clustering, Transactions of the Korean Institute of Electrical Engineers, vol 58(6) pp. 1230-1237, 2009
12. J. Luengo, S. Garcia, F. Herrera, A Study on the Use of Imputation Methods for Experimentation with Radial Basis Function Network Classifiers Handling Missing Attribute Values: The good synergy between RBFs and EventCovering method, Neural Networks, vol 23(3), pp. 406-418, 2010
13. Y.N. Hettiarachchi and H.L. Premaratne, A New Parameter Determining Mechanism for Radial Basis Neural Networks, In Proceedings of the 8th international Conference on Hybrid intelligent Systems, September 10-12, 2008
14. L. Chong, P. Yong-Jie, and L. Ye, 2009, Fuzzy neural network controller for AUV based on RAN, In Proceedings of the 21st Annual international Conference on Chinese Control and Decision Conference, Guilin, China, June 17 - 19, 2009
15. J.G. Juang, L.H. Chien and F. Lin, Automatic Landing Control System Design Using Adaptive Neural Network and Its Hardware Realization, IEEE Systems Journal, vol 5(2), pp. 266-277, 2011
16. Y. Wu, H. Wang, B. Zhang and K.L. Du, Using Radial Basis Function Networks for Function Approximation and Classification, ISRN Applied Mathematics, Article ID 324194, 2012
17. G.F. Pan, P. He, Y.T Zhou and W.X. Gao, Study of RAN and its application in temperature compensation for sensors, Proceedings of the 31st Chinese Control Conference Control Conference (CCC), pp. 3335-3340, 2012
18. V. Kurkova and P.C. Kainen, Comparing fixed and variable-width gaussian networks, Neural Networks, 2014
19. W. Qi,D. Li, Q. Jin, A resource-allocating network based on local conditions and its application in prediction of nonlinear systems, Proceedings of the World Congress on Intelligent Control and Automation (WCICA), 2012
20. X. Li, Z. Yang, F. Gu and C. Miao, Research and implementation of mine high voltage switch permanent magnetic actuator intelligent control technology, Proceedings of International Conference on Computer Design and Applications (ICCDA), 2010
21. W. Song, J.Z. Liang, X.L. He and P. Chen, Taking advantage of improved resource allocating network and latent semantic feature selection approach for automated text categorization, Applied Soft Computing, 2014
22. T. Kim and Y. Lee, A study on modified MLP learning using pretrained RBM, Proceedings of Korean Information Science Society Conference, vol 34(1), pp. 380-384, 2007
23. T. Kim, A study on modified MLP learning using pretrained RBM, Graduate Thesis, Yonsei University Graduate School of Computer Science, 2007
24. Cheng Junyu, Jin Qibing, Li Dazi, Qi Wenyuan, Quasi steady state compensation control based on RAN-LC prediction and its application, Computers and Applied Chemistry, issue 12, pp. 1521-1524, 2013
25. L.M. Dai, Y.L. Chen ,X. Liu, J.T. Zhou, F.M. Zhao, Y.H. Suo, W.B. Gao, D. Lou, A mineral resource potential mapping model based on RBF neural networks, Geophysical and Geochemical Exploration, vol 35(1), pp. 103-108, 2011
26. Y.D. Chung, H.S. Park, H.K. Kim and S.K. Oh, Nonlinear Characteristic Analysis of Charging Current for Linear Type Magnetic Flux Pump Using RBFNN, Journal of Korean Institute of Intelligent Systems, vol 20 (1), pp. 140-145, 2010
27. Zhang Y., Chen D., Han Y., Liu D., Surrogate model of radial basis function networks based on width factor sensitivity analysis, CAAI Transactions on Intelligent Systems, Vol 9, no 2, Apr 2014