Abstract
Due to its structural simplicity and good properties, radial basis function (RBF) neural network has increasingly been used in many areas for the solution of difficult real-world problems, especially the nonlinear system dynamic modeling. However, the major problem toward using RBF network is the appropriate selection of radial basis function parameters. The basis function parameters are in general the centers and the widths. Our attention in this paper is focused on the configuring the optimal set of parameters to make the networks small and efficient based on rough set theory (RST), which is a valid mathematical tool to perform data reduction. RST is first applied to extract the underlying rules from data. The condition components of the rules are then mapped into network centers. For improve performance, the width parameter of each hidden neuron is initialized individual. The valid of this algorithm is illustrated by an example on the modeling of a ship synchronous generator.
This work was supported by Science Project of Shanghai Education (04FA02, 05FZ06) and Shanghai Leading Academic Discipline Project (T0602).
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References
Kenne, G., Ahmed-Ali, T., et al.: Nonlinear systems parameters estimation using radial basis function network. Control Engineering Practice 14, 819–832 (2006)
Sarimveis, H., Doganis, P., Alexandridis, A.: A classification technique based on radial basis function neural networks. Advances in Engineering Software 37, 218–221 (2006)
Guo, J., Peter, B.: Selecting Input Factors for Clusters of Gaussian Radial Basis Function Networks to Improve Market Clearing Price Prediction. IEEE Transactions on Power Systems 18, 665–671 (2003)
Pawlak, Z.: Some Issues on Rough Sets. In: Peters, J.F., Skowron, A., Grzymała-Busse, J.W., Kostek, B.z., Świniarski, R.W., Szczuka, M. (eds.) Transactions on Rough Sets I. LNCS, vol. 3100, pp. 1–58. Springer, Heidelberg (2004)
Pawlak, Z.: Rough Sets and Intelligent Data Analysis. Information Sciences 147, 1–12 (2002)
Bazan, J.G., Szczuka, M.: The Rough Set Exploration System. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets III. LNCS, vol. 3400, pp. 37–56. Springer, Heidelberg (2005)
Xiao, J., Zhang, T.: New Rough Set Approach to Knowledge Reduction in Decision Table. In: Proceedings of the Third International Conference on Machine Learning and Cybernetics, pp. 2208–2211 (2004)
Wang, X., Zhang, T., Xiao, J.: Ship Synchronous Generator Modeling Based on RST and RBF Neural Networks. In: Wang, J., Yi, Z., Żurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3972, pp. 1363–1369. Springer, Heidelberg (2006)
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Zhang, T., Xiao, J., Wang, X., Ma, F. (2007). RST-Based RBF Neural Network Modeling for Nonlinear System. In: Liu, D., Fei, S., Hou, ZG., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4491. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72383-7_78
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DOI: https://doi.org/10.1007/978-3-540-72383-7_78
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