Abstract
In a previous work, the authors proposed, on one hand, an extension on the syntax of fuzzy rules by including new predicates and exceptional rules and, on the other hand, the use of an ant colony optimization algorithm to obtain an optimal set of such rules that describes an initial fuzzy model. The present work proposes several extensions on that algorithm in order to improve the interpretability of the obtained fuzzy model, as well as the computational cost of the algorithm. Experimental results on several initial fuzzy models reveal the gain obtained with each extension and when applied altogether.
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
Casillas, J., Cordón, O., Herrera, F., Magdalena, L. (eds.): Accuracy Improvements in Linguistic Fuzzy Modelling. Studies in Fuzziness and Soft Computing, vol. 129. Springer, Heidelberg (2003)
Castro, J., Castro-Schez, J., Zurita, J.: Learning maximal structure rules in fuzzy logic for knowledge acquisition in expert systems. Fuzzy Sets Syst. 101, 331–342 (1999)
Carmona, P., Castro, J., Zurita, J.: FRIwE: Fuzzy rule identification with exceptions. IEEE Trans. Fuzzy Syst. 12(1), 140–151 (2004)
Carmona, P., Castro, J., Zurita, J.: Learning maximal structure fuzzy rules with exceptions. Fuzzy Sets Syst. 146(1), 63–77 (2004)
Carmona, P., Castro, J.: An Ant Colony Optimization plug-in to enhance the interpretability of fuzzy rule bases with exceptions. In: Analysis and Design of Intelligent Systems Using Soft Computing Techniques. Advances in Soft Computing, vol. 41, pp. 436–444. Springer, Heidelberg (2007)
Dorigo, M., Colorni, A., Maniezzo, V.: The ant system: Optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. B 26(1), 29–41 (1996)
Dorigo, M., Stützle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)
Dorigo, M., Gambardella, L.: Ant colony system: A cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comput. 1(1), 53–66 (1997)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Carmona, P., Castro, J.L. (2008). An Improved ACO Based Plug-in to Enhance the Interpretability of Fuzzy Rule Bases with Exceptions. In: Dorigo, M., Birattari, M., Blum, C., Clerc, M., Stützle, T., Winfield, A.F.T. (eds) Ant Colony Optimization and Swarm Intelligence. ANTS 2008. Lecture Notes in Computer Science, vol 5217. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87527-7_2
Download citation
DOI: https://doi.org/10.1007/978-3-540-87527-7_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87526-0
Online ISBN: 978-3-540-87527-7
eBook Packages: Computer ScienceComputer Science (R0)