Abstract
In this work we extendthe Cooperative Rules learning methodology to improve simple linguistic fuzzy models, including the learning of rule weights within the rule cooperation paradigm. Considering these kinds of techniques could result in important improvements of the system accuracy, maintaining the interpretability to an acceptable level.
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
Alcalá, R., Casillas, J., Cordón, O., Herrera, F.: Improvement to the cooperative rules methodology by using the ant colony system algorithm. Mathware & Soft Computing 8:3 (2001) 321–335
Casillas, J., Cordón, O., Herrera, F.: COR: A methodology to improve ad hoc datadriven linguistic rule learning methods by inducing cooperation among rules. IEEE Transactions on Systems, Man, andCyb ernetics-Part B: Cybernetics (2002). To appear
Casillas, J., Cordón, O., Herrera, F.: Different approaches to induce cooperation in fuzzy linguistic models under the COR methodology. In: Bouchon-Meunier, B., Gutiérrez-Ríos, J., Magdalena, L., Yager, R.R. (Eds.): Techniques for Constructing Intelligent Systems. Springer-Verlag, Heidelberg, Germany (2002)
Cho, J.S., Park, D.J.: Novel fuzzy logic control basedon weighting of partially inconsistent rules using neural network. Journal of Intelligent Fuzzy Systems 8 (2000) 99–110
Cordón, O., Herrera, F., Sánchez, L.: Solving electrical distribution problems using hybridev olutionary data analysis techniques. AppliedIn telligence 10 (1999) 5–24
Cordón, O., Herrera, F.: A proposal for improving the accuracy of linguistic modeling. IEEE Transactions on Fuzzy Systems 8:4 (2000) 335–344
Ishibuchi, H., Nozaki, K., Yamamoto, N., Tanaka, H.: Selecting fuzzy if-then rules for classification problems using genetic algorithms. IEEE Transactions on Fuzzy Systems 9:3 (1995) 260–270
Pal, N.R., Pal, K.: Handling of inconsistent rules with an extended model of fuzzy reasoning. Journal of Intelligent Fuzzy Systems 7 (1999) 55–73
Pardalos, P.M., Resende, M.G.C.: Handbook of applied optimization. Oxford University Press, NY (2002)
Wang, L.X., Mendel, J.M.: Generating fuzzy rules by learning from examples. IEEE Transactions on Systems, Man, andCyb ernetics 22 (1992) 1414–1427
Yu, W., Bien, Z.: Design of fuzzy logic controller with inconsistent rule base. Journal of Intelligent Fuzzy Systems 2 (1994) 147–159
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Alcalá, R., Casillas, J., Cordón, O., Herrera, F. (2002). Improving Simple Linguistic Fuzzy Models by Means of the Weighted COR Methodology. In: Garijo, F.J., Riquelme, J.C., Toro, M. (eds) Advances in Artificial Intelligence — IBERAMIA 2002. IBERAMIA 2002. Lecture Notes in Computer Science(), vol 2527. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36131-6_30
Download citation
DOI: https://doi.org/10.1007/3-540-36131-6_30
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-00131-7
Online ISBN: 978-3-540-36131-2
eBook Packages: Springer Book Archive