Abstract
The present paper proposes a novel idea used to conceive a proper fuzzy logic controller for an industrial application. Generally, the design of fuzzylogic controller is treated as numerical optimisation problem. Genetic algorithms and neural networks are the main techniques used to adjust the values of fuzzy logic controller parameters. The majority of approaches start with an empty fuzzy logic controller, having default parameters, and make improvement over the previous one. This operation is not really a design activity; it’sbetter called a learning process. In this work we regard the conception of a fuzzy logic controller as creative designing problem. We import from AI-creative-design community three methods: decomposition, co-evolution and emergence; we apply these techniques to combine elementary components in order to generate a fuzzy logic controller.
Chapter PDF
References
S. Mitra and Y. Hayashi, “Neuro-fuzzy rule generation: survey in soft computing framework,” IEEE Transactions on Neural Networks, Vol. 11, 2000, pp. 748–768.
M. M. Gupta and D. H. Rao, “On the principles of fuzzy neural networks,” Fuzzy Sets and Systems, Vol. 61, 1994, pp. 1–18.
C. Karr, “Genetic algorithms for fuzzy controllers,” AI Expert, Vol. 6, 1991, pp.26–33.
C. L. Karr and E. J. Gentry, “Fuzzy control of pH using genetic algorithms,” IEEE Transaction on Fuzzy Systems, Vol. 1, 1993, pp. 46–53
M. Regattieri Delgado, F. Von Zuben, and F. Gomide. “Hierarchical Genetic Fuzzy Systems”. Information Sciences-Special Issue on Recent Advances in Genetic Fuzzy Systems, 136(1–4):29–52, 2001.
Jinwoo Kim-“A Framework for Multiresolution Optimization in a Parallel/Distributed Environment: Simulation of Hierarchical GAs” Journal of Parallel and Distributed Computing, 32(1), pp. 90–102, January 1996.
H. Simon-“Problem forming, problem finding and problem solving in design”, dans Collen(A.) et Gasparski (W), 1995, p. 245–257.
M. Fustier-‘La résolution de problèmes:méthodologie de l’action’, Paris, Editions ESF et Librairies Techniques, 1989.
H. Simon “The structure of ill structured problems”, Artificial Intelligence, 4, 1973, p. 181–201.
Gero, J. S. and Maher, M. L. “Modeling Creativity and Knowledge-Based Creative Design”, Lawrence Erlbaum, Hillsdale, NJ. (1993)
Inform Software Corp., fuzzy-TECH User’s 1996.
Jyh-Shing Roger Jang “ANFIS: Adaptive-Network-Based Fuzzy Inference System”, IEEE Transactions on Systems, Man, and Cybernetics 1993
Gero, J. & Yan, M. (1994). Shape emergence by symbolic reasoning, Environment and Planning B: Planning and Design 21: 191–218.
Gero, J., Damski, J. & Jun, H. (1995). Emergence in caad systems, in M. Tan & R. Teh (eds), The Global Design Studio, Centre for Advanced Studies of Architecture, National University of Singapore, pp. 423–438.
Edmonds, E. & Soufi, B. (1992). The computational modelling of emergent shapes in design, in J. S. Gero & F. Sudweeks (eds), pp. 173–189.
Gero, J. & Schnier, T. (1995). Evolving representation of design cases and their use in creative design, in J. Gero & F. Sudweeks (eds), pp. 343–368.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer Science + Business Media, Inc.
About this chapter
Cite this chapter
Hamrouni, L., Alimi, A.M. (2004). Creative Design of Fuzzy Logic Controller. In: Bramer, M., Devedzic, V. (eds) Artificial Intelligence Applications and Innovations. AIAI 2004. IFIP International Federation for Information Processing, vol 154. Springer, Boston, MA. https://doi.org/10.1007/1-4020-8151-0_8
Download citation
DOI: https://doi.org/10.1007/1-4020-8151-0_8
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4020-8150-7
Online ISBN: 978-1-4020-8151-4
eBook Packages: Springer Book Archive