Abstract
Interpretability is one of the indispensable features of fuzzy models. This paper discusses the interpretability of fuzzy models with/without prior knowledge about the target system. Without prior knowledge, conciseness of fuzzy models helps humans to interpret their input-output relationships. In the case where a human has the knowledge in advance, an interpretable model could be the one that explicitly explains his/her knowledge. Experimental results show that the concise model has the essential interpretable feature. The results also show that human’s knowledge changes the most interpretable model from the most concise model.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
S. Matsushita and T. Furuhashi, et al., “Selection of Input Variables Using Genetic Algorithm for Hierarchical Fuzzy Modeling,” Proc. of 1996 First Asia-Pacific Conference on Simulated Evolution and Learning, pp.106–113, 1996.
H. Akaike, “Information Theory and an Extension of the Maximum Likelihood Principle,” 2nd International Symposium on Information Theory, pp.267–281, 1973.
H. Nomura, S. Araki, I. Hayashi and N. Wakami, “A Learning Method of Fuzzy Reasoning by Delta Rule,” Proc. of Intelligent System Symposium, pp.25–30, 1992.
M. Setnes and R. Babuska and H.B. Verbruggen, “Rule-Based Modeling: Precision and Transparency,” IEEE Trans. Syst., Man, Cybern., pt.C, Vol. 28, No. 1, pp.165–169, Feb. 1998.
J. Valente de Oliveira, “Semantic Constraints for Membership Function Optimization,” IEEE Trans. Syst., Man, Cybern., pt.A, Vol. 23, No. 1, pp.128–138, Jan., 1999.
M. Setnes and H. Roubos, “GA-Fuzzy Modeling and Classification: Complexity and Performance,” IEEE Trans. Fuzzy Syst., Vol. 8, No. 5, pp.509–522, Oct. 2000.
A. De Luca and S. Termini, “A Definition of a Nonprobabilistic Entropy in the Setting of Fuzzy Sets Theory,” Information and Control, Vol. 20, pp.301–312, 1972.
M. Mizumoto, “Fuzzy Control Under Various Approximate Reasoning Methods,” Proc. of Second IFSA Congress, pp.143–146, 1987.
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
Furuhashi, T. (2002). On Interpretability of Fuzzy Models. In: Pal, N.R., Sugeno, M. (eds) Advances in Soft Computing — AFSS 2002. AFSS 2002. Lecture Notes in Computer Science(), vol 2275. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45631-7_2
Download citation
DOI: https://doi.org/10.1007/3-540-45631-7_2
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-43150-3
Online ISBN: 978-3-540-45631-5
eBook Packages: Springer Book Archive