Abstract
A voting-mechanism-based fuzzy neural network system is proposed in this paper. When constructing the network structure, a generalized class cover problem is presented and its two solving algorithm, an improved greedy algorithm and a binary particle swarm optimization algorithm, are proposed to get the class covers with relatively even radii, which are used to partition fuzzy input space and extract fewer robust fuzzy IF-THEN rules. Meanwhile, a weighted Mamdani inference mechanism is adopted to improve the efficiency of the system output and a real-valued particle swarm optimization-based algorithm is used to refine the system parameters. Experimental results show that the system is feasible and effective.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Yanxin, H., Chunguang, Z.: Recognizing Taste Signals Using A Clustering-based Fuzzy Neural Network. Chinese Journal of Electronics 14(1), 21–25 (2005)
Cannon, A., Cowen, L.: Approximation Algorithms for the Class Cover Problem. Annals of Mathematics and Artificial Intelligence 40(3), 215–223 (2004)
Priebe, C.E., Marchette, D.J., DeVinney, J., Socolinsky, D.: Classification using Class Cover Catch Digraphs. Journal of Classification 20(1), 3–23 (2003)
Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proceeding of IEEE International Conference on Neural Networks, Perth, Australia, vol. IV, pp. 1942–1948. IEEE Press, Los Alamitos (1995)
Van Den Bergh, F.: An Analysis of Particle Swarm Optimizers (PH.D thesis). Pretoria: Natural and Agricultural Science Department, University of Pretoria (2001)
Kennedy, J., Eberhart, R.C.: A Discrete Binary Version of the Particle Swarm Algorithm. In: Proceedings of the 1997 Conference on Systems, Man, and Cybernetics, Piscataway, NJ, pp. 4104–4109. IEEE Press, Los Alamitos (1997)
Jyh-Shing, R.J., Chuen-Tsai, S., Eiji, M.: Neuro-Fuzzy and Soft computing. Xi An Jiaotong University Press, Xi An (2000)
Ludmila, I.K.: How Good are Fuzzy If-Then Classifiers? IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 30(4), 501–509 (2000)
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
Huang, Y., Wang, Y., Zhou, W., Yu, Z., Zhou, C. (2005). A Fuzzy Neural Network System Based on Generalized Class Cover and Particle Swarm Optimization. In: Huang, DS., Zhang, XP., Huang, GB. (eds) Advances in Intelligent Computing. ICIC 2005. Lecture Notes in Computer Science, vol 3645. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11538356_13
Download citation
DOI: https://doi.org/10.1007/11538356_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28227-3
Online ISBN: 978-3-540-31907-8
eBook Packages: Computer ScienceComputer Science (R0)