Abstract
In this paper, a hybrid algorithm based on tabu search (TS) algorithm and least squares (LS) algorithm, is proposed to generate an appropriate fuzzy rule set automatically by structure and parameters optimization of fuzzy neural network. TS is used to tune the structure and membership functions simultaneously, after which LS is used for the consequent parameters of the fuzzy rules. A simulation for a nonlinear function approximation is presented and the experimental results show that the proposed algorithm can generate fewer rules with a lower average percentage error.
Supported by key project of Chinese Ministry of Education (104262) and fund project of Chongqing Science and Technology Commission (2003–7881), China.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Setnes, M., Koene, A., Babuska, R., Bruijn, P.M.: Data-Driven Initialization and Structure Learning in Fuzzy Neural Networks. In: IEEE World Congress on Computational Intelligence, Fuzzy Systems Proceedings, vol. 2, pp. 1147–1152 (1998)
Ma, M., Xu, Y., Zhang, L.: Automatic Fuzzy Rule Extraction Based Weighted Fuzzy Neural Networks. Computer Application 23, 15–17 (2003) (in Chinese with English Abstract)
Zhou, Z.J., Mao, Z.Y.: On Designing an Optimal Fuzzy Neural Network Controller Using Genetic Algorithms. In: Proceedings of the 3rd World Congress on Intelligent Control and Automation, Hefei, China, pp. 391–395 (2000)
Glover, F., Laguna, M.: Tabu Search. Kluwer Academic Publishers, Boston (1997)
Lin, Y.H., Cunningham III, G.A.: A New Approach to Fuzzy-neural System Modeling. IEEE Trans. on Fuzzy Syst. 3, 190–198 (1995)
Liu, G., Fang, Y., Zheng, X., et al.: Tuning Neuro-fuzzy Function Approximator by Tabu Search. In: International Symposium on Neural Networks, Part 1, Dalian, China, pp. 276–281 (2004)
He, Y., Liu, G., Qiu, Y.: A Novel Adaptive Search Strategy of Intensification and Diversification in Tabu Search. Journal of Computer Research and Development 41, 162–166 (2004) (in Chinese with English Abstract)
Jang, J.-S.R.: ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE Trans. on Syst., Man and Cybernetics 23, 665–684 (1993)
Wu, S., Er, M.J.: A Fast Approach for Automatic Generation of Fuzzy Rules by Generalized Dynamic Fuzzy Neural Networks. IEEE Trans. on Fuzzy Syst. 9, 578–594 (2001)
Sexton, R.S., Alidaee, B., Dorsey, R.E., et al.: Global Optimization for Artificial Neural Networks: A Tabu Search Application. European Journal of Operational Research 106, 570–584 (1998)
Takagi, T., Sugeno, M.: Fuzzy Identification of Systems and Its Applications to Modeling and Control. IEEE Trans. on Syst., Man and Cybernetics 15, 116–132 (1985)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Xiao, L., Liu, G. (2005). Automatic Fuzzy Rule Extraction Based on Fuzzy Neural Network. In: Wang, J., Liao, X., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427391_110
Download citation
DOI: https://doi.org/10.1007/11427391_110
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25912-1
Online ISBN: 978-3-540-32065-4
eBook Packages: Computer ScienceComputer Science (R0)