Abstract
We describe in this paper the use of hierarchical genetic algorithms for fuzzy system optimization in intelligent control. In particular, we consider the problem of optimizing the number of rules and membership functions using an evolutionary approach. The hierarchical genetic algorithm enables the optimization of the fuzzy system design for a particular application. We illustrate the approach with the case of intelligent control in a medical application. Simulation results for this application show that we are able to find an optimal set of rules and membership functions for the fuzzy system.
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
O. Castillo and P. Melin (2001), “Soft Computing for Control of Non-Linear Dynamical Systems”, Springer-Verlag, Heidelberg, Germany.
O. Castillo and P. Melin (2003), “Soft Computing and Fractal Theory for Intelligent Manufacturing”, Springer-Verlag, Heidelberg, Germany.
J. Holland, (1975), “Adaptation in natural and artificial systems” (University of Michigan Press).
A. Homaifar and E. McCormick (1995), “Simultaneous design of membership functions and rule sets for fuzzy controllers using genetic algorithms”, IEEE Trans. Fuzzy Systems, vol. 3, pp. 129–139.
J.-S. R. Jang and C.-T. Sun (1995) “Neurofuzzy fuzzy modeling and control”, Proc. IEEE, vol. 83, pp. 378–406.
J.-S. R. Jang, C.-T. Sun, and E. Mizutani (1997), “Neuro-fuzzy and Soft Computing, A computational approach to learning and machine intelligence”, , Prentice Hall, Upper Saddle River, NJ.
C.L. Karr and E.J. Gentry (1993), “Fuzzy control of pH using genetic algorithms”, IEEE Trans. Fuzzy Systems, vol. 1, pp. 46–53.
A. Lozano (2003), “Optimizatión de un Sistema de Control Difuso por medio de algoritmos genéticos jerarquicos”, Thesis, Dept of Computer Science, Tijuana Institute of Technology, Mexico.
K.F. Man, K.S. Tang, and S. Kwong (1999), “Genetic Algorithms: Concepts and Designs”, Springer Verlag.
P. Melin and O. Castillo (2002), “Modelling, Simulation and Control of Non-Linear Dynamical Systems”, Taylor and Francis, London, Great Britain.
T.J. Procyk and E.M. Mamdani (1979), “A linguistic self-organizing process controller” Automatica, vol. 15, no. 1, pp 15–30.
K.-S. Tang, K.-F. Man, Z.-F. Liu and S. Kwong (1998), “Minimal fuzzy memberships and rules using hierarchical genetic algorithms”, IEEE Trans, on Industrial Electronics, vol. 45, no. 1.
J. Yen, and R. Langari (1999), “Fuzzy Logic: intelligence, control and information”, Prentice Hall, Inc.
T. Yoshikawa, T. Furuhashi and Y. Uchikawa (1996), “Emergence of effective fuzzy rules for controlling mobile robots using NA coding method”, Proc. ICEC′96, Nagoya, Japan, pp. 581–586.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer
About this paper
Cite this paper
Castillo, O., Huesca, G., Valdez, F. (2006). Evolutionary Computing for Topology Optimization of Fuzzy Systems in Intelligent Control. In: Abraham, A., de Baets, B., Köppen, M., Nickolay, B. (eds) Applied Soft Computing Technologies: The Challenge of Complexity. Advances in Soft Computing, vol 34. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31662-0_48
Download citation
DOI: https://doi.org/10.1007/3-540-31662-0_48
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31649-7
Online ISBN: 978-3-540-31662-6
eBook Packages: EngineeringEngineering (R0)