Hostname: page-component-78c5997874-j824f Total loading time: 0 Render date: 2024-11-05T09:29:02.980Z Has data issue: false hasContentIssue false
  • English
  • Français

Iterative genetic algorithm for learning efficient fuzzy rule set

Published online by Cambridge University Press:  01 November 2003

MENG HIOT LIM  and
WILLIE NG
MENG HIOT LIM
Affiliation:
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore
WILLIE NG
Affiliation:
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore
Get access Rights & Permissions [Opens in a new window]

Abstract

We present a methodology of learning fuzzy rules using an iterative genetic algorithm (GA). The approach incorporates a scheme of partitioning the entire solution space into individual subspaces. It then employs a mechanism to progressively relax or tighten the constraint. The relaxation or tightening of constraint guides the GA to the subspace for further iteration. The system referred to as the iterative GA learning module is useful for learning an efficient fuzzy control algorithm based on a predefined linguistic terms set. The overall approach was applied to learn a fuzzy algorithm for a water bath temperature control. The simulation results demonstrate the effectiveness of the approach in automating an industrial process.

Keywords

Fuzzy RulesGenetic LearningIterative Genetic AlgorithmPlant AutomationProcess Control
Type
Research Article
Information
AI EDAM , Volume 17 , Issue 4 , November 2003 , pp. 335 - 347
Copyright
2003 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Cordon, O., Herrera, F., Hoffmann, F., & Magdalena, L. (2001). Genetic Fuzzy Systems: Evolutionary Turning and Learning of Fuzzy Knowledge Bases. Singapore: World Scientific.CrossRef
Goldberg, D.E. & Lingle, R. (1985). Alleles, loci and the travelling salesman problem. Proc. First Int. Conf. Genetic Algorithms and Their Applications, pp. 154–158.
Goldberg, D.E. (1989). Genetic Algorithms in Search, Optimisation and Machine Learning, pp. 76–79. New York: Addison–Wesley.
Hoffmann, F. & Pfister, G. (1997). Evolutionary Design of a Fuzzy Knowledge Base for a Mobile Robot. International Journal of Approximate Reasoning, 17(4), 447–469.CrossRefGoogle Scholar
Homaifar, A. & McCormick, E. (1995). Simultaneous design of membership functions and rule sets for fuzzy controllers using genetic algorithms. IEEE Transactions on Fuzzy Systems, 3, 129–139.CrossRef
Juang, C.F., Lin, J.Y., & Lin, C.T. (2000). Genetic Reinforcement Learning through Symbiotic Evolution for Fuzzy Controller Design. IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics, 30(2), 290–301.CrossRefGoogle Scholar
Karr, C.L. (1991). Design of an adaptive fuzzy logic controller using a genetic algorithm. Proc. Fourth Int. Conf. Genetic Algorithms, pp. 450–457.
Khalid, M. & Omatu, S. (1992). A neural network controller for a temperature control system. IEEE Control Systems Magazine, 12, 58–64.CrossRefGoogle Scholar
Lim, M.H., Rahardja, S., & Gwee, B.H. (1996). A GA paradigm for learning fuzzy rules. Fuzzy Sets and Systems, 82, 177–186.CrossRef
Lim, M.H., Yuan, Y., & Omatu, S. (2000). Efficient genetic algorithms using simple genes exchange local search policy for the quadratic assignment problem. Computational Optimisation and Applications, 15(3), 249–268.CrossRefGoogle Scholar
Lin, C.T., Juang, C.F., & Li, C.P. (1999). Temperature control with a neural fuzzy inference network. IEEE Transactions on Systems, Man, and Cybernetics, 29, 440–451.
Magdalena, L. (1998). Crossing unordered sets of rules in evolutionary fuzzy controllers. International Journal of Intelligent Systems, 13(10–11), 993–1010.3.0.CO;2-U>CrossRefGoogle Scholar
Ng, K.C. & Li, Y. (1994). Design of sophisticated fuzzy logic controllers using genetic algorithms. Proc. Third IEEE Int. Conf. Fuzzy Systems, 3, 1708–1712.
Ng, W.L. & Lim, M.H. (2002). Genetic optimisation of fuzzy rule set for industrial plant automation. Fourth Asia–Pacific Conf. Simulated Evolution and Learning, pp. 627–631.
Tanomaru, J. & Omatu, S. (1992). Process control by on-line trained neural controllers. IEEE Transactions on Industrial Electronics, 39, 511–521.CrossRef
Thrift, P. (1991). Fuzzy logic synthesis with genetic algorithms. Proc. Fourth Int. Conf. Genetic Algorithms, pp. 509–513.