Abstract
There are many papers in the literature that deal with the problem of the design of a fuzzy system from a set of given training examples. Those who get the best approximation accuracy are based on TSK fuzzy rules, which have the problem of not being as interpretable as Mamdany-type Fuzzy Systems. A question now is posed: How can the interpretability of the generated fuzzy rule-table base be increased? A possible response is to try to reduce the rule-base size by generalizing fuzzy-rules consequents which are symbolic functions instead of fixed scalar values or polynomials, and apply symbolic regressions technics in fuzzy system generation. A first approximation to this idea is presented in this paper for 1-D and 2D functions.
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
Funabashi, M., Maeda, A.: Fuzzy and neural hybrid expert systems: synergetic AI. IEEE Expert, 32–40 (1995)
Jang, J.S.R., Sun, C.T., Mizutani, E.: Neuro-Fuzzy and soft computing. Prentice Hall, Englewood Cliffs (1997) ISBN 0-13-261066-3
Kosko, B.: Neural Networks and Fuzzy Systems. Prentice-Hall, Englewood Cliffs (1992)
Koza, J.R.: Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge (1992)
Pomares, H., Rojas, I., Ortega, J., Gonzalez, J., Prieto, A.: A Systematic Approach to a Self-Generating Fuzzy Rule-Table for Function Approximation. IEEE Trans. on Syst. Man. and Cyber. 30(3) (June 2000)
Rojas, I., Merelo, J.J., Bernier, J.L., Prieto, A.: A new approach to fuzzy controller designing and coding via genetic algorithms. In: IEEE International Conference on Fuzzy Systems, Barcelona, pp. 1505–1510 (July 1997)
Rovatti, R., Guerrieri, R.: Fuzzy sets of rules for system identification. IEEE Trans.on Fuzzy Systems 4(2), 89–102 (1996)
Sudkamp, T., Hammell, R.J.: Interpolation, Completion, and Learning Fuzzy Rules. IEEE Trans. on Syst. Man and Cyber. 24(2), 332–342 (1994)
Wang, L.X., Mendel, J.M.: Generating fuzzy rules by learning from examples. IEEE Trans. On Syst. Man and Cyber. 22(6), 1414–1427 (1992)
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
Rubio, G., Pomares, H., Rojas, I., Guillen, A. (2005). A Basic Approach to Reduce the Complexity of a Self-generated Fuzzy Rule-Table for Function Approximation by Use of Symbolic Regression in 1D and 2D Cases. In: Mira, J., Álvarez, J.R. (eds) Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach. IWINAC 2005. Lecture Notes in Computer Science, vol 3562. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11499305_15
Download citation
DOI: https://doi.org/10.1007/11499305_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26319-7
Online ISBN: 978-3-540-31673-2
eBook Packages: Computer ScienceComputer Science (R0)