Advanced SearchSearch Tips
A Learning Algorithm for Optimal Fuzzy Control Rules
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
A Learning Algorithm for Optimal Fuzzy Control Rules
Chung, Byeong-Mook;
  PDF(new window)
A fuzzy learning algorithm to get the optimal fuzzy rules is presented in this paper. The algorithm introduces a reference model to generate a desired output and a performance index funtion instead of the performance index table. The performance index funtion is a cost function based on the error and error-rate between the reference and plant output. The cost function is minimized by a gradient method and the control input is also updated. In this case, the control rules which generate the desired response can be obtained by changing the portion of the error-rate in the cost funtion. In SISO(Single-Input Single- Output)plant, only by the learning delay, it is possible to experss the plant model and to get the desired control rules. In the long run, this algorithm gives us the good control rules with a minimal amount of prior informaiton about the environment.
Fuzzy Control;Learning Control;Optimal Control;Model Reference Fuzzy Control;Regulation Control;
 Cited by
Automatica, 1979. vol.15. 1, pp.15-30

IEEE Trans. on Systems, Man, and Cybernetics, 1985. vol.15. 1, pp.116-132

Fuzzy Sets and Systems, 1988. vol.26. pp.151-164

IEE Pro. D., 1992. vol.139. 5, pp.460-464

Fuzzy Sets and Systems, 1993. vol.59. 1, pp.1-14

IEEE Trans. on Systems, Man, and Cybernetics, 1990. vol.20. 2, pp.404-435

IEEE Trans. on Systems, Man and Cybernetics, 1983. vol.13. 5, pp.834-846

Journal of Intelligent & Fuzzy Systems, 1993. vol.1. 4, pp.335-349