Hybrid Artificial Bee Colony and Least Squares Method for Rule-Based Systems Learning
This paper deals with the problem of automatic rule
generation for fuzzy systems design. The proposed approach is based
on hybrid artificial bee colony (ABC) optimization and weighted least
squares (LS) method and aims to find the structure and parameters of
fuzzy systems simultaneously. More precisely, two ABC based fuzzy
modeling strategies are presented and compared. The first strategy
uses global optimization to learn fuzzy models, the second one
hybridizes ABC and weighted least squares estimate method. The
performances of the proposed ABC and ABC-LS fuzzy modeling
strategies are evaluated on complex modeling problems and compared
to other advanced modeling methods.
[1] D.T. Pham, D. Karaboga, Intelligent optimisation techniques, Springer
(2000).
[2] S. Cao, N. W. Rees et G. Feng, Analysis and design for a class of complex
control systems – Part I: Fuzzy modelling and identification, Automatica,
33(1997), 1017-1028.
[3] D. Karaboga, An idea based on honey bee swarm for numerical
optimization. Technical Report-TR06, Erciyes University, Engineering
Faculty, Computer Engineering Department, (2005).
[4] D. Karaboga, B. Basturk, A powerful and efficient algorithm for
numerical function optimization: artificial bee colony (ABC) algorithm,
Journal of Global optimization, 39 (2007), 459-471.
[5] L. Zhao, F. Qjan, Y. Yang, Y. Zeng, H. Su, Automatically extracting TS
fuzzy models using cooperative random learning particle swarm
optimization, Applied Soft Computing, 10(2010), 938-944.
[6] H. Habbi, M. Kidouche, M. Zelmat, Data-driven fuzzy models for
nonlinear identification of a complex heat exchanger, Applied
Mathematical Modeling, 35 (2011), 1470-1482.
[7] C. Lee, Fuzzy logic in control systems, IEEE Transactions on systems,
man and cybernetics, 20(1990), 419-435.
[8] H. Habbi, M. Zelmat., B. Ould Bouamama, A dynamic fuzzy model for a
drum-boiler-turbine system, Automatica, 39 (2003), 1213-1219.
[9] U. Maulik, S. Bandyopadhyay, Genetic algorithm-based clustering
technique, Pattern recognition, 33(2000), 1455-1465.
[10] M. Gendreau, J. Potvin, Handbook of metaheuristics, Springer (2010).
[11] H. Habbi, Artificial bee colony optimization algorithm for TS-type fuzzy
systems learning, 25th International Conference of European Chapter on
Combinatorial Optimization, ECCO XXV, April 26-28, (2012), Antalya,
Turkey
[12] M. Kim, C. Kim, J. Lee, Evolving compact and interpretable
Takagi-Sugeno fuzzy models with a new encoding scheme, IEEE
Transactions on systems, man and cybernetics, 36(2006), 1006-1022.
[13] J. Abonyi, B. Feil, Cluster analysis for data mining and system
identification, Birkhauser (2007).
[14] H. Habbi, M. Kidouche, M. Kinnaert, M. Zelmat, Fuzzy model-based
fault detection and diagnosis for a pilot heat exchanger, International
Journal of Systems Science, 42(2011), 587-599.
[15] A.F. Gomez-Skarmeta, M. Delgado, M.A. Vila, About the use of fuzzy
clustering techniques for fuzzy model identification, Fuzzy sets and
systems, 106 (1999), 179-188.
[16] A. Fink, M. Fischer, O. Nelles, R. Isermann, Supervision of nonlinear
adaptive controllers based on fuzzy models, Control Engineering
Practice. 8 (2000), 1093-1105.
[17] Z. Su, P. Wang, J. Shen, Y. Zhang, L. Chen, Convenient T-S fuzzy model
with enhanced performance using a novel swarm intelligent fuzzy
clustering technique, Journal of Process Control 22 (2012), 108-124.
[18] C.W. Xu, Z. Yong, Fuzzy model identification and self-learning for
dynamic systems, IEEE Transactions on Systems, Man and Cybernetics
17(4) (1987), 683-689.
[19] J.Q. Chen, Y.G. Xi, et al., A clustering algorithm for fuzzy model
identification, Journal of Fuzzy Sets and Systems, 38 (1998), 319-329.
[20] N. Li, S.Y. Li, Y.G. Xi, Multi-model modeling method based on
satisfactory clustering, Control Theory & Application 20(5) (2003),
783-787.
[21] G. Zhu, S. Kwong, Gbest-guided artificial bee colony algorithm for
numerical function optimization, 217(2010), 3166-3173.
[1] D.T. Pham, D. Karaboga, Intelligent optimisation techniques, Springer
(2000).
[2] S. Cao, N. W. Rees et G. Feng, Analysis and design for a class of complex
control systems – Part I: Fuzzy modelling and identification, Automatica,
33(1997), 1017-1028.
[3] D. Karaboga, An idea based on honey bee swarm for numerical
optimization. Technical Report-TR06, Erciyes University, Engineering
Faculty, Computer Engineering Department, (2005).
[4] D. Karaboga, B. Basturk, A powerful and efficient algorithm for
numerical function optimization: artificial bee colony (ABC) algorithm,
Journal of Global optimization, 39 (2007), 459-471.
[5] L. Zhao, F. Qjan, Y. Yang, Y. Zeng, H. Su, Automatically extracting TS
fuzzy models using cooperative random learning particle swarm
optimization, Applied Soft Computing, 10(2010), 938-944.
[6] H. Habbi, M. Kidouche, M. Zelmat, Data-driven fuzzy models for
nonlinear identification of a complex heat exchanger, Applied
Mathematical Modeling, 35 (2011), 1470-1482.
[7] C. Lee, Fuzzy logic in control systems, IEEE Transactions on systems,
man and cybernetics, 20(1990), 419-435.
[8] H. Habbi, M. Zelmat., B. Ould Bouamama, A dynamic fuzzy model for a
drum-boiler-turbine system, Automatica, 39 (2003), 1213-1219.
[9] U. Maulik, S. Bandyopadhyay, Genetic algorithm-based clustering
technique, Pattern recognition, 33(2000), 1455-1465.
[10] M. Gendreau, J. Potvin, Handbook of metaheuristics, Springer (2010).
[11] H. Habbi, Artificial bee colony optimization algorithm for TS-type fuzzy
systems learning, 25th International Conference of European Chapter on
Combinatorial Optimization, ECCO XXV, April 26-28, (2012), Antalya,
Turkey
[12] M. Kim, C. Kim, J. Lee, Evolving compact and interpretable
Takagi-Sugeno fuzzy models with a new encoding scheme, IEEE
Transactions on systems, man and cybernetics, 36(2006), 1006-1022.
[13] J. Abonyi, B. Feil, Cluster analysis for data mining and system
identification, Birkhauser (2007).
[14] H. Habbi, M. Kidouche, M. Kinnaert, M. Zelmat, Fuzzy model-based
fault detection and diagnosis for a pilot heat exchanger, International
Journal of Systems Science, 42(2011), 587-599.
[15] A.F. Gomez-Skarmeta, M. Delgado, M.A. Vila, About the use of fuzzy
clustering techniques for fuzzy model identification, Fuzzy sets and
systems, 106 (1999), 179-188.
[16] A. Fink, M. Fischer, O. Nelles, R. Isermann, Supervision of nonlinear
adaptive controllers based on fuzzy models, Control Engineering
Practice. 8 (2000), 1093-1105.
[17] Z. Su, P. Wang, J. Shen, Y. Zhang, L. Chen, Convenient T-S fuzzy model
with enhanced performance using a novel swarm intelligent fuzzy
clustering technique, Journal of Process Control 22 (2012), 108-124.
[18] C.W. Xu, Z. Yong, Fuzzy model identification and self-learning for
dynamic systems, IEEE Transactions on Systems, Man and Cybernetics
17(4) (1987), 683-689.
[19] J.Q. Chen, Y.G. Xi, et al., A clustering algorithm for fuzzy model
identification, Journal of Fuzzy Sets and Systems, 38 (1998), 319-329.
[20] N. Li, S.Y. Li, Y.G. Xi, Multi-model modeling method based on
satisfactory clustering, Control Theory & Application 20(5) (2003),
783-787.
[21] G. Zhu, S. Kwong, Gbest-guided artificial bee colony algorithm for
numerical function optimization, 217(2010), 3166-3173.
@article{"International Journal of Information, Control and Computer Sciences:68457", author = "Ahcene Habbi and Yassine Boudouaoui", title = "Hybrid Artificial Bee Colony and Least Squares Method for Rule-Based Systems Learning", abstract = "This paper deals with the problem of automatic rule
generation for fuzzy systems design. The proposed approach is based
on hybrid artificial bee colony (ABC) optimization and weighted least
squares (LS) method and aims to find the structure and parameters of
fuzzy systems simultaneously. More precisely, two ABC based fuzzy
modeling strategies are presented and compared. The first strategy
uses global optimization to learn fuzzy models, the second one
hybridizes ABC and weighted least squares estimate method. The
performances of the proposed ABC and ABC-LS fuzzy modeling
strategies are evaluated on complex modeling problems and compared
to other advanced modeling methods.
", keywords = "Automatic design, learning, fuzzy rules, hybrid,
swarm optimization.", volume = "8", number = "12", pages = "2155-4", }
