Auto Regressive Tree Modeling for Parametric Optimization in Fuzzy Logic Control System
The advantage of solving the complex nonlinear
problems by utilizing fuzzy logic methodologies is that the
experience or expert-s knowledge described as a fuzzy rule base can
be directly embedded into the systems for dealing with the problems.
The current limitation of appropriate and automated designing of
fuzzy controllers are focused in this paper. The structure discovery
and parameter adjustment of the Branched T-S fuzzy model is
addressed by a hybrid technique of type constrained sparse tree
algorithms. The simulation result for different system model is
evaluated and the identification error is observed to be minimum.
[1] T. Takagi and M. Sugeno, "Fuzzy identification of systems and its
application to modeling and control", IEEE Trans. Syst., Man, Cybern.,
Vol.15, 116-132, 1985.
[2] Badgwell T. A. and Qin S. J., "Review of nonlinear model predictive
control applications, chapter 1", In: Kouvaritakis B. and Cannon M.,
Nonlinear predictive control: theory and practice, IEE Control
Engineering Series 61, The Institution of Electrical Engineers, London,
2001.
[3] J.J. Buckley et al., "Fuzzy input-output controller are universal
approximators", Fuzzy Sets and Systems, Vol.58, pp.273-278, 1993.
[4] W. Duch, R. Setiono, and J. Zurada, "Computational intelligence
methods for rule-based data understanding," Proc. IEEE, vol. 92, no. 5,
May 2004.
[5] W. Duch, R. Adamczak, and K. Grabczewski, "A new methodology of
extraction, optimization and application of crisp and fuzzy logical rules,"
IEEE Trans. Neural Netw., vol. 12, no. 2, pp. 277-306, Mar. 2001.
[6] H. Ying, "General Takagi-Sugeno "fuzzy systems with simplified linear
rule consequent are universal controllers models and filters",
Information Science, Vol.108, pp.91-107, 1998.
[7] H. Ying, "Theory and application of a novel fuzzy PID controller using
a simplified Takagi-Sugeno rule scheme", Information Science, Vol.123,
pp.281-293, 2000.
[8] C. Xu and Y. liu, "Fuzzy model identification and self learning for
dynamic systems", IEEE Trans. Syst., Man, Cybern., Vol.17, pp.683-
689, 1987.
[9] Q. Gan and C.J. Harris, "Fuzzy local linearization and logic basis
function expansion in nonlinear system modeling", IEEE Trans. on
Systems, Man, and Cybernetics-Part B, Vol.29, N0.4, 1999.
[10] S. Yuhui et al., "Implementation of evolutionary fuzzy systems", IEEE
Trans. Fuzzy Systems, Vol.7, No.2, pp.109-119, 1999.
[11] K. Sin-Jin et al., "Evolutionary design of fuzzy rule base for nonlinear
system modeling and control", IEEE Trans. Fuzzy Systems, Vol.8, No.1,
pp.37-45, 2000.
[12] H. Yo-Ping et al., "Designing a fuzzy model by adaptive macroevolution
genetic algorithms", Fuzzy Sets and Systems, Vol.113, pp.367-379,
2000.
[13] W. Baolin et al., "Fuzzy modeling and identification with genetic
algorithm based learning", Fuzzy Sets and Systems, Vol.113, pp.352-
365, 2000.
[14] D. Maurizio et al., "Learning fuzzy rules with tabu search-an application
to control", IEEE Trans. on Fuzzy Systems, Vol.7, No.2, pp.295-318,
1999.
[15] G.V.S. Raju et al., "Hierarchical fuzzy control", Int. J. Contr., Vol.54,
No.5, pp.1201-1216, 1991.
[16] L.X. Wang, "Universal approximation by hierarchical fuzzy systems",
Fuzzy Sets and Systems, Vol.93, pp.223-230, 1998.
[17] O. Huwendiek et al., "Function approximation with decomposed fuzzy
systems", Fuzzy Sets and Systems, Vol.101, pp.273-286, 1999.
[18] Al Seyab R. K. and Cao Y., "Nonlinear system identification for
predictive control using continuous time recurrent neural networks and
automatic differentiation", Submitted for publication in: IEEE TNN
Special Issue on Feedback Control, September, 2005.
[19] C. Wei and Li-Xin Wang, "A note on universal approximation by
hierarchical fuzzy systems", Information Science, Vol.123, pp.241-248,
2000.
[20] Yongquan Y., Ying H., Minghui W., Bi Z., Guokun Z., "Fuzzy neural
PID controller and tuning its weight factors using genetic algorithm
based on different location crossover", IEEE International Conference
on Systems, Man and Cybernetics pp. 3709-3713, 2004.
[1] T. Takagi and M. Sugeno, "Fuzzy identification of systems and its
application to modeling and control", IEEE Trans. Syst., Man, Cybern.,
Vol.15, 116-132, 1985.
[2] Badgwell T. A. and Qin S. J., "Review of nonlinear model predictive
control applications, chapter 1", In: Kouvaritakis B. and Cannon M.,
Nonlinear predictive control: theory and practice, IEE Control
Engineering Series 61, The Institution of Electrical Engineers, London,
2001.
[3] J.J. Buckley et al., "Fuzzy input-output controller are universal
approximators", Fuzzy Sets and Systems, Vol.58, pp.273-278, 1993.
[4] W. Duch, R. Setiono, and J. Zurada, "Computational intelligence
methods for rule-based data understanding," Proc. IEEE, vol. 92, no. 5,
May 2004.
[5] W. Duch, R. Adamczak, and K. Grabczewski, "A new methodology of
extraction, optimization and application of crisp and fuzzy logical rules,"
IEEE Trans. Neural Netw., vol. 12, no. 2, pp. 277-306, Mar. 2001.
[6] H. Ying, "General Takagi-Sugeno "fuzzy systems with simplified linear
rule consequent are universal controllers models and filters",
Information Science, Vol.108, pp.91-107, 1998.
[7] H. Ying, "Theory and application of a novel fuzzy PID controller using
a simplified Takagi-Sugeno rule scheme", Information Science, Vol.123,
pp.281-293, 2000.
[8] C. Xu and Y. liu, "Fuzzy model identification and self learning for
dynamic systems", IEEE Trans. Syst., Man, Cybern., Vol.17, pp.683-
689, 1987.
[9] Q. Gan and C.J. Harris, "Fuzzy local linearization and logic basis
function expansion in nonlinear system modeling", IEEE Trans. on
Systems, Man, and Cybernetics-Part B, Vol.29, N0.4, 1999.
[10] S. Yuhui et al., "Implementation of evolutionary fuzzy systems", IEEE
Trans. Fuzzy Systems, Vol.7, No.2, pp.109-119, 1999.
[11] K. Sin-Jin et al., "Evolutionary design of fuzzy rule base for nonlinear
system modeling and control", IEEE Trans. Fuzzy Systems, Vol.8, No.1,
pp.37-45, 2000.
[12] H. Yo-Ping et al., "Designing a fuzzy model by adaptive macroevolution
genetic algorithms", Fuzzy Sets and Systems, Vol.113, pp.367-379,
2000.
[13] W. Baolin et al., "Fuzzy modeling and identification with genetic
algorithm based learning", Fuzzy Sets and Systems, Vol.113, pp.352-
365, 2000.
[14] D. Maurizio et al., "Learning fuzzy rules with tabu search-an application
to control", IEEE Trans. on Fuzzy Systems, Vol.7, No.2, pp.295-318,
1999.
[15] G.V.S. Raju et al., "Hierarchical fuzzy control", Int. J. Contr., Vol.54,
No.5, pp.1201-1216, 1991.
[16] L.X. Wang, "Universal approximation by hierarchical fuzzy systems",
Fuzzy Sets and Systems, Vol.93, pp.223-230, 1998.
[17] O. Huwendiek et al., "Function approximation with decomposed fuzzy
systems", Fuzzy Sets and Systems, Vol.101, pp.273-286, 1999.
[18] Al Seyab R. K. and Cao Y., "Nonlinear system identification for
predictive control using continuous time recurrent neural networks and
automatic differentiation", Submitted for publication in: IEEE TNN
Special Issue on Feedback Control, September, 2005.
[19] C. Wei and Li-Xin Wang, "A note on universal approximation by
hierarchical fuzzy systems", Information Science, Vol.123, pp.241-248,
2000.
[20] Yongquan Y., Ying H., Minghui W., Bi Z., Guokun Z., "Fuzzy neural
PID controller and tuning its weight factors using genetic algorithm
based on different location crossover", IEEE International Conference
on Systems, Man and Cybernetics pp. 3709-3713, 2004.
@article{"International Journal of Information, Control and Computer Sciences:55178", author = "Arshia Azam and J. Amarnath and Ch. D. V. Paradesi Rao", title = "Auto Regressive Tree Modeling for Parametric Optimization in Fuzzy Logic Control System", abstract = "The advantage of solving the complex nonlinear
problems by utilizing fuzzy logic methodologies is that the
experience or expert-s knowledge described as a fuzzy rule base can
be directly embedded into the systems for dealing with the problems.
The current limitation of appropriate and automated designing of
fuzzy controllers are focused in this paper. The structure discovery
and parameter adjustment of the Branched T-S fuzzy model is
addressed by a hybrid technique of type constrained sparse tree
algorithms. The simulation result for different system model is
evaluated and the identification error is observed to be minimum.", keywords = "Fuzzy logic, branch T-S fuzzy model, tree modeling,
complex nonlinear system.", volume = "3", number = "1", pages = "91-5", }
