Abstract
Fuzzy systems are currently used in many kinds of applications, such as control, for their effectiveness and efficiency. However, these characteristics depend primarily on the model yield by human experts, which may or may not be optimized for the problem at hand. Particle swarm optimization (PSO) is a technique used in complex problems, including multi-objective problems. In this paper, we propose an algorithm that can generate fuzzy systems automatically for different kinds of problems by simply providing the objective function and the problem variables. This automatic generation is performed using PSO. To be able to do so and in order to avoid dealing with inconsistent fuzzy systems, we used some known techniques, such as the WM method, to help in developing meaningful rules and clustering concepts to generate membership functions. Tests using the sigmoid 3D curve have been carried out and the obtained results are presented.
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
Beni, G., Wang, J.: Robots and Biological Systems: Towards a New Bionics? Toscana, Italy. NATO ASI Series (1989)
Chen, L., Chen, C.L.P.: Pre-shaped fuzzy c-means algorithm (pfcm) for transparent membership function generation. In: IEEE International Conference on Systems, Man and Cybernetics, pp. 789–794 (October 2007)
Cintra, M.E., de Arruda Camargo, H.: Fuzzy rules generation using genetic algorithms with self-adaptive selection. In: IEEE International Conference on Information Reuse and Integration, pp. 261–266 (August 2007)
Cordn, O., Herrera, F.: A hybrid genetic algorithm-evolution strategy process for learning fuzzy logic controller knowledge bases. In: Genetic Algorithms and Soft Computing, pp. 251–278. Physica-Verlag, Heidelberg (1996)
Engelbrecht, A.P.: Fundamentals of Computational Swarm Intelligence. John Wiley and Sons Ltd., England (2005)
Guo, B., Liang, X., Wang, B., Wan, L.: Sigmoid surface control for mini underwater vehicles by improved particle swarm optimization. In: International Conference on Robotics and Biomimetics (December 2007)
Kim, M.-S., Kim, C.-H., Lee, J.j.: Evolving compact and interpretable takagi-sugeno fuzzy models with a new encoding scheme. IEEE Transactions on Systems, Man and Cybernetics, Part B 36, 1006–1023 (2006)
Krone, A., Slawinski, T.: Data-based extraction of unidimensional fuzzy sets for fuzzy rulegeneration. In: IEEE World Congress on Computational Intelligence, IEEE International Conference on Fuzzy Systems, vol. 2, pp. 1032–1037 (May 1998)
Setnes, M., Roubos, H.: GA-fuzzy modeling and classification: complexity and performance. IEEE Transactions on Fuzzy Systems 8, 509–522 (2000)
Wang, L.-X.: The WM method completed: A flexible fuzzy system approach to data mining. IEEE Transactions on Fuzzy Systems 11, 768–782 (2003)
Zadeh, L.A.: Fuzzy sets. Information and Control 8, 338–353 (1965)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Costa, S.O., Nedjah, N., de Macedo Mourelle, L. (2010). Automatic Modeling of Fuzzy Systems Using Particle Swarm Optimization. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2010. Lecture Notes in Computer Science(), vol 6113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13208-7_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-13208-7_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13207-0
Online ISBN: 978-3-642-13208-7
eBook Packages: Computer ScienceComputer Science (R0)