Abstract
In knowledge representation by fuzzy rule based systems two reasoning mechanisms can be distinguished: conjunction-based and implication-based inference. Both approaches have complementary advantages and drawbacks depending on the structure of the knowledge that should be represented. Implicative rule bases are less sensitive to incompleteness of knowledge. However, implication-based inference has not been widely used. This disregard is probably due to the lack of suitable methods for the automated acquisition of implicative fuzzy rules. In this paper a genetic programming based approach for the data-driven extraction of implicative fuzzy rules is presented. The proposed algorithm has been applied to the acquisition of rule bases for the design of reinforced concrete structural members. Finally an outlook on the application of the presented approach within a machine learning environment for evolutionary design and optimization of complex structural systems is given.
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
Schnellenbach-Held, M., Freischlad, M.: Fuzzy Rule Based Models for Slab System Design. In: Schnellenbach-Held, M., Denk, H. (eds.) Advances in Intelligent Computing in Engineering. Proceedings of 9th International EG-ICE Workshop, pp. 134–143. VDI-Fortschritt-Berichte, Darmstadt (2002)
Dubois, D., Prade, H., Ughetto, L.: A new perspective on reasoning with fuzzy rules. International Journal of Intelligent Systems 18(5), 541–567 (2003)
Cordón, O., Gomide, F., Herrera, F., Hoffmann, F., Magdalena, L.: Ten years of genetic fuzzy systems: current framework and new trends. Fuzzy Sets and Systems 141(1), 5–31 (2004)
Freischlad, M., Schnellenbach-Held, M.: A machine learning approach for the support of preliminary structural design. Advanced Engineering Informatics 19(4), 281–287 (2005)
Jin, Y., von Seelen, W., Sendhoff, B.: On Generating FC3 Fuzzy Rule Systems from Data Using Evolution Strategies. IEEE Transactions on Systems, Man and Cybernetics, Part B 29(4), 829–845 (1999)
Fonseca, C.M., Fleming, P.J.: Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization. In: Forrest, S. (ed.) Genetic Algorithms: Proceedings of the Fifth International Conference, pp. 416–423. Morgan Kaufmann, San Mateo (1993)
Pullmann, T., Skolicki, Z., Freischlad, M., Arciszewski, T., De Jong, K., Schnellenbach-Held, M.: Structural Design of Reinforced Concrete Tall Buildings: Evolutionary Computation Approach Using Fuzzy Sets. In: Ciftcioglu, Ö., Dado, E. (eds.) Intelligent Computing in Engineering, Foundation of Design Research SOON, pp. 53–61 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Freischlad, M., Schnellenbach-Held, M., Pullmann, T. (2006). Evolutionary Generation of Implicative Fuzzy Rules for Design Knowledge Representation. In: Smith, I.F.C. (eds) Intelligent Computing in Engineering and Architecture. EG-ICE 2006. Lecture Notes in Computer Science(), vol 4200. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11888598_22
Download citation
DOI: https://doi.org/10.1007/11888598_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46246-0
Online ISBN: 978-3-540-46247-7
eBook Packages: Computer ScienceComputer Science (R0)