Hierarchical genetic algorithms for topology optimization in fuzzy control systems

International Journal of General Systems

Volumn 36, Issue 5, 2007, Pages 575-591

  Castillo, Oscar ^a   Valdez, Fevrier ^a   Melin, Patricia ^a  

^a TIJUANA INSTITUTE OF TECHNOLOGY   (Mexico)

Author keywords

Fuzzy control system; Hierarchical genetic algorithms; Membership functions; Neural network

Indexed keywords

GENETIC ALGORITHMS; HIERARCHICAL SYSTEMS; INTELLIGENT CONTROL; MEMBERSHIP FUNCTIONS; NEURAL NETWORKS; OPTIMIZATION; PROBLEM SOLVING; TOPOLOGY;

HIERARCHICAL GENETIC ALGORITHMS; OPTIMAL SET;

FUZZY SYSTEMS;

EID: 35648931073     PISSN: 03081079     EISSN: 15635104     Source Type: Journal    
DOI: 10.1080/03081070701321860     Document Type: Article

References (27)

1. BARUCH, I.S. and GARRIDO, R. 2005, A direct adaptive neural control scheme with integral terms. International Journal of Intelligent Systems, 20(2), pp. 213-224.
2. CASTILLO, O. and MELIN, P., 2001, Soft Computing for Control of Non-Linear Dynamical Systems (Heidelberg, Germany: Springer-Verlag).
3. CASTILLO, O. and MELIN, P., 2003. Soft Computing and Fractal Theory for Intelligent Manufacturing (Heidelberg, Germany: Springer-Verlag).
4. CASTILLO, O., HUESCA, G. and VALDEZ, F., 2004. Evolutionary computing for fuzzy system optimization in intelligent control. Proceedings of IC-AI'04, Las Vegas, USA, vol. 1, pp. 98-104.
5. CURATOLO, M., DERIGHETTI, M., PETERSEN- FELIX, S., FEIGENWINTER, P., FISCHER, M. and ZBINDEN, A.M., 1996, Fuzzy logic control of inspired isoflurance and oxygen concentrations using minimal flow anaesthesia. British Journal of Anaesthesia, 76, pp. 245-250.
6. DAVIS, L., 1991, Handbook of Genetic Algorithms (New York: Van Nostrand Reinhold).
7. GOLDBERG, D., 1989, in Genetic Algorithms in Search, Optimization andMachine Learning, Addison Wesley (Ed.).
8. HOLLAND, J., 1975, Adaptation in Natural and Artificial Systems (MI: University of Michigan Press).
9. HOMAIFAR, A. and MCCORMICK, E., 1995, Simultaneous design of membership functions and rule sets for fuzzy controllers using genetic algorithms. IEEE Transactions Fuzzy Systems, 3, pp. 129-139.
10. JANG, J.-S.R. and SUN, C.-T., 1995, Neurofuzzy fuzzy modeling and control. Proceeding of the IEEE, 83, pp. 378-406.
11. JANG, J.-S.R., SUN, C.-T. and MIZUTANI, E., 1997, Neuro-fuzzy and Soft Computing, A Computational Approach to Learning and Machine Intelligence (Upper Saddle River, NJ: Prentice Hall).
12. KARR, C.L. and GENTRY, E.J., 1993, Fuzzy control of pH using genetic algorithms. IEEE Transaction Fuzzy Systems, 1, pp. 46-53.
13. LANGARI, R., 1990, A framework for analysis and synthesis of fuzzy linguistic control systems, Ph.D. thesis, University of California, Berkeley.
14. LOZANO, A., 2004, Optimization de un Sistema de control Difuso por medio de algoritmos genéticos jerarquicos, Thesis, Dept. of Computer Science, Tijuana Institute of Technology, Mexico.
15. MAN, K.F., TANG, K.S. and KWONG, S., 1999, Genetic Algorithms: Concepts and Designs (New York: Springer Verlag).
16. MELIN, P. and CASTILLO, O., 2002, Modelling, Simulation and Control of Non-Linear Dynamical Systems (London, Great Britain: Taylor and Francis).
17. NAWA, N.E. and FURUHASHI, T., 1999, Fuzzy system parameters discovery by bacterial evolutionary algorithm. IEEE Transaction Fuzzy Systems, 7, pp. 608-616.
18. PROCYK, T.J. and MAMDANI, E.M., 1979, A linguistic self-organizing process controller. Automatica, 15(1), pp. 15-30.
19. SALMERI, M., RE, M., PETRONGARI, E. and CARDARILLI, G.C., 1999, A novel bacterial algorithm to extract the rule base from, a training set, Dept. of Electronic Engineering, University of Rome.
20. SEPULVEDA, R., CASTILLO, O., MELIN, P., MONTIEL, O. and RODRIGUEZ-DIAZ, A., 2005, Handling uncertainty in controllers using type-2 fuzzy logic. Journal of Intelligent Systems, 14, pp. 237-262.
21. TANG, K.-S., MAN, K.-.F., LIU, Z.-F. and KWONG, S., 1998, Minimal fuzzy memberships and rules using hierarchical genetic algorithms. IEEE Transaction on Industrial Electronics, 45(1).
22. VALD.ES, M., GOMEZ-SKARMETA, A.F. and BOTIA, J.A., 2005, Toward a framework for the specification of hybrid fuzzy modeling. International Journal of Intelligent Systems, 20(2), pp. 225-252.
23. VALDEZ, F. and CASTILLO, O., 2004. Comparative study of evolutionary computing methods for fuzzy system optimization in intelligent control. Proc. IS-IC04, Tijuana, B.C, México, pp. 1-5.
24. YEN, J. and LANGARI, R., 1999, Fuzzy Logic: Intelligence, Control and Information (Englewood Cliffs: Prentice Hall, Inc).
25. YOSHIKAWA, T., FURUHASHI, T. and UCHIKAWA, Y., 1996. Emergence of effective fuzzy rules for controlling mobile robots using DNA coding method. Proc. ICEC'96, Nagoya, Japan, pp. 581-586.
26. ZADEH, L., 1965, Fuzzy sets. Journal of Information and Control, 8, pp. 338-353.
27. ZADEH, L., 1987, in Fuzzy Sets and Applications R. R. Yager, S. Ovchinnikov, R. M. Tong and H. T. Nguyen, (Eds.) (Nueva York: John Wiley).