Abstract
Radial Basis Function Neural Networks (RBFNs) are quite popular due to their ability to discover and approximate complex nonlinear dependencies within the data under analysis. The performance of the RBF network depends on numerous factors. One of them is a value of the RBF shape parameter. This parameter has a direct impact on performance of the transfer function of each hidden unit. Values of the transfer function parameters, including the value of its shape, are set during the RBFN tuning phase. Setting values of the transfer function parameters, including its shape can be viewed as the optimization problem in which the performance of the considered RBFN is maximized. In the paper the agent-based population learning algorithm finding the optimal or near optimal value of the RBF shape parameter is proposed and evaluated.
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References
Asuncion, A., Newman, D.J.: UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine, CA (2007), http://www.ics.uci.edu/~mlearn/MLRepository.html
Barbucha, D., et al.: e-JABAT - An Implementation of the Web-Based A-Team. In: Nguyen, N.T., Jain, I.C. (eds.) Intel. Agents in the Evol. of Web and Appl. SCI, vol. 167, pp. 57–86. Springer, Heidelberg (2009)
Broomhead, D.S., Lowe, D.: Multivariable Functional Interpolation and Adaptive Networks. Complex Systems 2, 321–355 (1988)
Czarnowski, I., Jędrzejowicz, P.: An agent-based approach to ANN training. Knowledge-Based Systems 19, 304–308 (2006)
Czarnowski, I., Jędrzejowicz, P.: An Approach to Cluster Initialization for RBF Networks. In: Graña, M., Toro., C., Posada, J., Howlett, R., Jain, L.C. (eds.) Advances in Knowledge-Based and Intelligent Information and Engineering Systems. Frontiers in Artificial Intelligence and Applications, vol. 243, pp. 1151–1160. IOS Press (2012)
Czarnowski, I.: Cluster-based Instance Selection for Machine Classification. Knowledge and Information Systems 30(1), 113–133 (2012)
Datasets used for classification: comparison of results. Directory of data sets, http://www.is.umk.pl/projects/datasets.html (accessed September 1, 2009)
Duch, W., Jankowski, N.: Transfer Functions: Hidden Possibilities for Better Neural Networks. In: Proceedings of the 9th European Symposium on Artificial Neural Networks (ESANN), Brugge, pp. 81–94 (2001)
Fasshauer, G.E., Zhang, J.G.: On Choosing ”Optimal” Shape Parameters for RBF Approximation. Numerical Algorithms 45(1-4), 345–368 (2007)
Gao, H., Feng, B., Hou, Y., Zhu, L.: Training RBF Neural Network with Hybrid Particle Swarm Optimization. In: Wang, J., Yi, Z., Żurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3971, pp. 577–583. Springer, Heidelberg (2006)
Hanrahan, G.: Artificial Neural Networks in Biological and Envoronmental Analysis. Analytical Chemistry Series. CRC Press, Taylor & Francis Group (2011)
Hoffmann, G.A.: Adaptive Transfer Functions in Radial Basis Function (RBF) Networks. In: Bubak, M., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2004. LNCS, vol. 3037, pp. 682–686. Springer, Heidelberg (2004)
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), 57–67 (2005)
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), 1411–1423 (2006)
Sánchez, A.V.D.: Searching for a solution to the automatic RBF network design problem. Neurocomputing 42(1-4), 147–170
Talukdar, S., Baerentzen, L., Gove, A., de Souza, P.: Asynchronous Teams: Co-operation Schemes for Autonomous, Computer-Based Agents. Technical Report EDRC 18-59-96, Carnegie Mellon University, Pittsburgh (1996)
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, 433–454 (2012)
Yonaba, H., Anctil, F., Fortin, V.: Comparing Sigmoid Transfer Functions for Neural Network Multistep Ahead Streamflow Forecasting. Journal of Hydrologic Engineering 15(4), 275–283 (2010)
Zhang, D., Tian, Y., Zhang, P.: Kernel-based Nonparametric Regression Method. In: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, pp. 410–413 (2008)
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Czarnowski, I., Jędrzejowicz, P. (2013). Agent-Based Population Learning Algorithm for RBF Network Tuning. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2013. Lecture Notes in Computer Science(), vol 7894. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38658-9_4
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DOI: https://doi.org/10.1007/978-3-642-38658-9_4
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