Designing RBF Networks Using the Agent-Based Population Learning Algorithm

1. Asuncion, A. and Newman, D.J., “UCI Machine Learning Repository,” Irvine, CA: University of California, School of Information and Computer Science, http://www.ics.uci.edu/mlearn/MLRepository.html. 2007, Accessed 24 June 2009.

2. Barbucha, D., Czarnowski, I., Jȩdrzejowicz, P., Ratajczak-Ropel, E., Wierzbowska, I., “e-JABAT - An Implementation of the Web-Based A-Team,” Intelligent Agents in the Evolution of Web and Applications (N.T. Nguyen and I.C. Jain, eds.), Studies in Computational Intelligence, 167, pp. 57–86, Springer Berlin/Heidelberg, 2009.

3. Barbucha, D., Czarnowski, I., Jȩdrzejowicz, P., Ratajczak-Ropel, E., Wierzbowska, I., “Influence of the Working Strategy on A-Team Performance,” in Smart Information and Knowledge Management (Szczerbicki, E., Nguyen, N.T., eds.), Studies in Computational Intelligence, 260, Springer-Verlag Berlin Heidelberg, pp. 83–102, 2010.

4. Broomhead, D. S., Lowe D., “Multivariable Functional Interpolation and Adaptive Networks,” Complex Systems, 2, pp. 321–355, 2007.

   MathSciNet  Google Scholar 

5. Chen, S., Hong, X., Harris, C. J., “Particle Swarm Optimization Aided Orthogonal Forwarded Regression for Unified Data Modeling,” IEEE Transactions on Evolutionary Computation, 14, 4, pp. 477–499, 2012.

   Article  Google Scholar 

6. Czarnowski, I., Jȩdrzejowicz, P., “An agent-based approach to ANN training,” Knowledge-Based Systems, 19, pp. 304–308, 2006.

   Article  Google Scholar 

7. Czarnowski, I., Jȩdrzejowicz, P., “Selecting a Representative Data Set of the Required Size Using the Agent-Based Population Learning Algorithm,” Cybernetics and Systems, 43, 4, pp. 303–318, 2012.

   Article  Google Scholar 

8. Czarnowski, I. and Jȩdrzejowicz, P., “Agent-based Approach to RBF Network Training with Floating Centroids,” in ICCCI 2012 LNAI 7653 (Nguyen, N. T., Kiem, H., Jȩdrzejowicz, P., eds.), Springer-Verlag Berlin Heidelberg, 2012.

9. Czarnowski, I., “Cluster-based Instance Selection for Machine Classification,” Knowledge and Information Systems, 30, 1, pp. 113–133, 2012.

   Article  Google Scholar 

10. Czarnowski, I., Jȩdrzejowicz, P., “An Approach to Cluster Initialization for RBF Networks,” in Advances in Knowledge-Based and Intelligent Information and Engineering Systems, Frontiers in Artificial Intelligence and Applications (Grana, M., Toro., C., Posada, J., Howlett, R., Jain, L.C. eds.), 243, IOS Press, pp. 1151–1160, 2012.

11. Czarnowski, I., Jȩdrzejowicz, P., “Agent-Based Approach to the Design of RBF Networks,” Cybernetics and Systems, 44, 2-3, pp. 155–172, 2013.

    Article  Google Scholar 

12. Czarnowski, I., Jȩdrzejowicz, P., “Agent-Based Population Learning Algorithm for RBF Network Tuning,” in Artificial Intelligence and Soft Computing, LNAI Part I. LNAI 7894 (Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L. A., Zurada, J. M. eds.), Springer-Verlag Berlin Heidelberg, pp. 41–51, 2013.

13. “Datasets used for classification: comparison of results,” in Directory of Data Sets, http://www.is.umk.pl/projects/datasets.html. Accessed 1 Sep 2009.

14. Deng, J., Li, K., Irwin, G.W., “Locally regularized two-stage learning algorithm for RBF network centre selection,” International Journal of System Science, 43, 6, pp. 1157–1170, 2012.

    Article  MathSciNet  Google Scholar 

15. Duch, W., Jankowski, N., “Transfer Functions: Hidden Possibilities for Better Neural Networks,” in Proc. of the 9th European Symposium on Artificial Neural Networks (ESANN), Brugge, pp. 81–94, 2011.

16. Fasshauer, G. E., Zhang, J. G., “On Choosing “Optimal” Shape Parameters for RBF Approximation,” Numerical Algorithms, 45, 1-4, pp. 345–368, 2007.

    Article  MathSciNet  MATH  Google Scholar 

17. Gao, H., Feng, B., Zhu, L., “Training RBF Neural Network with Hybrid Particle Swarm Optimization” in ISNN 2006, LNCS 3971 (Weng, J. et al. eds.), Springer-Verlag Berlin Heidelberg, pp. 577–583, 2006.

18. Garca, S., Molina, D., Lozano, M., Herrera, F., “A Study on the Use of Non-Parametric Tests for Analyzing the Evolutionary Algorithms’ Behaviour: A Case Study on the CEC’ 2005 Special Session on Real Parameter Optimization,” Journal of Heuristics, 15, pp. 617–644, 2009.

    Article  Google Scholar 

19. Hanrahan, G., Artificial Neural Networks in Biological and Environmental Analysis, Analytical Chemistry Series, CRC Press, Taylor & Francis Group, 2011.

20. Hoffmann, G. A., “Adaptive Transfer Functions in Radial Basis Function (RBF) Networks,” in ICCS 2004, LNCS 3037 (Bubak, M. et al. eds.), Springer-Verlag Heidelberg, pp. 682–686, 2004.

21. Huang, G-B., Saratchandra, P., Sundararajan, N., “A Generalized Growing and Pruning RBF (GGAP-RBF) Neural Network for Function Approximation,” IEEE Transactions on Neural Networks, 16, 1, pp. 57–67, 2005.

    Article  Google Scholar 

22. Jȩdrzejowicz, P., “Social learning algorithm as a tool for solving some difficult scheduling problems,” Foundation of Computing and Decision Sciences, 24, pp. 51–66, 1999.

    Google Scholar 

23. Jȩdrzejowicz, J., Jȩdrzejowicz, P., “Cellular GEP-Induced Classifiers,” in ICCCI 2010, Part I, LNAI 6421 (Pan J.-S., Chen S.-M. and Nguyen, N. T. eds.), Springer-Verlag Berlin Heidelberg, pp. 343–352, 2004.

24. Krishnaiah, P. R., Kanal, L. N., Handbook of Statistics 2: Classification, Pattern Recognition and Reduction of Dimensionality, North Holland, Amsterdam, 1982.

    Google Scholar 

25. Król, D., Nguyen, N.T., “Special Issue on Knowledge Integration and Management in Autonomous Systems,” Journal of Intelligent and Fuzzy Systems, 21, 3, pp. 163–164, 2010.

    Google Scholar 

26. Kuncheva, L., “Initializing of an RBF Network by a Genetic Algorithm,” Neurocomputing, 14, 3, pp. 273–288, 1997.

    Article  Google Scholar 

27. Liang, N-Y., Huang, G-B., Saratchandran, P., Sundararajan, N., “A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks,” IEEE Transactions on Neural Networks, 17, 6, pp. 1411–1423, 2006.

    Article  Google Scholar 

28. Michalewicz, Z., Genetic Algorithms + Data Structures = Evolution Programs, Springer, Berlin, 1996.

    Book  MATH  Google Scholar 

29. Qasem, S. N., Shamsuddin, S. M. H., “Radial Basis Function Network Based on Multi-Objective Particle Swarm Optimization,” in Proc. of the 6th International Symposium on Mechatronics and its Applications (ISMA09), Sharjah, UAE, March 24-26, 2009.

30. Ros, F., Pintore, M., Chretie, J. R., “Automatic Design of Growing Radial Basis Function Neural Networks Based on Neighborhood Concepts,” Chemometric-sand Intelligent Laboratory Systems, 87, pp. 231–240, 2007.

    Article  Google Scholar 

31. Sanchez, A.V.D., “Searching for a solution to the automatic RBF network design problem,” Neurocomputing, 42, 1-4, pp. 147–170, 2002.

    Article  MATH  Google Scholar 

32. “The European Network of Excellence on Intelligence Technologies for Smart Adaptive Systems (EUNITE)” - EUNITE World Competition in domain of Intelligent Technologies - http://neuron.tuke.sk/competition2 (accessed on 1 September 2002).

33. Wang, L., Yang, B., Chen, Y., Abraham, A., Sun, H., Chen, Z., Wang, H., “Improvement of Neural Network Classifier Using Floating Centroids,” Knowledge Information Systems, 31, pp. 433–454, 2012.

    Article  Google Scholar 

34. Wang, H-Q., Huang, D-S., “Non-linear Cancer Classification Using a Modified Radial Basis Function Classification Algorithm,” Journal of Biomedical Science, 12, 5, pp. 819–826, 2005.

    Article  Google Scholar 

35. Wei, L.Y., Sundararajan, N., Saratchandran, P., “Performance Evaluation of a Sequential Minimal Radial Basis Function (RBF) Neural Network Learning Algorithm,” IEEE Transactions on Neural Networks, 9, pp. 308–318, 1998.

    Article  Google Scholar 

36. Wilson, D.R., Martinez, T.R., “Reduction Techniques for Instance-based Learning Algorithm,” Machine Learning, 33, 3, pp. 257–286, 1998.

    Google Scholar 

37. Talukdar, S., Baeretzen, L., Gove, A., de Souza, P., “Asynchronous teams: Cooperation schemes for autonomous agents,” Journal of Heuristics, 4, pp. 295–321, 1998.

    Article  Google Scholar 

38. Shang, Y. and Wah, B.W., “A Global Optimization Method for Neural Network Training,” in Proc. of the Conference of Neural Networks, 29, IEEE Computer, pp. 45–54, 1996.

39. Yonaba, H., Anctil, F., Fortin, V., “Comparing Sigmoid Transfer Functions for Neural Network Multistep Ahead Streamflow Forecasting,” Journal of Hydrologic Engineering, 15, 4, pp. 275–283, 2010.

    Article  Google Scholar 

40. Zhang, D., Tian, Y., Zhang, P., “Kernel-based Nonparametric Regression Method,” in Proc. of the IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, pp. 410–413, 2008.

Download references