GPFIS - control : a genetic fuzzy system for control tasks

• Autorzy: Koshiyama A. S. , Vellasco M. M. B. R. , Tanscheit R.

Identyfikatory

DOI

10.1515/jaiscr-2015-0006

• Języki publikacji: EN

Abstrakty

EN

This work presents a Genetic Fuzzy Controller (GFC), called Genetic Programming Fuzzy Inference System for Control tasks (GPFISControl). It is based on MultiGene Genetic Programming, a variant of canonical Genetic Programming. The main characteristics and concepts of this approach are described, as well as its distinctions from other GFCs. Two benchmarks application of GPFISControl are considered: the CartCentering Problem and the Inverted Pendulum. In both cases results demonstrate the superiority and potentialities of GPFISControl in relation to other GFCs found in the literature.

Słowa kluczowe

EN

genetic fuzzy controler; GFC; genetic programming fuzzy inference system for control; GPFISControl; multigene genetic programming; inverted pendulum

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2014
• Tom: Vol. 4, No. 3
• Strony: 167--179
• Opis fizyczny: Bibliogr. 35 poz., rys.

Twórcy

autor

Koshiyama A. S.

• Department of Electrical Engineering, Pontifical Catholic University of Rio de Janeiro Rua Marqus de So Vicente, 225, Gvea – Rio de Janeiro, RJ, Brazil

autor

Vellasco M. M. B. R.

• Department of Electrical Engineering, Pontifical Catholic University of Rio de Janeiro Rua Marqus de So Vicente, 225, Gvea – Rio de Janeiro, RJ, Brazil

autor

Tanscheit R.

• Department of Electrical Engineering, Pontifical Catholic University of Rio de Janeiro Rua Marqus de So Vicente, 225, Gvea – Rio de Janeiro, RJ, Brazil

Bibliografia

• [1] J. M. Mendel, Fuzzy logic systems for engineering: a tutorial, Proceedings of the IEEE, Vol.83, No.3, 1995, p.345-377.
• [2] C. Elmas, C., O. Deperlioglu, and H. H. Sayan, Adaptive fuzzy logic controller for DC–DC converters, Expert Systems with Applications, Vol.36, No.2, 2009, pp.1540-1548.
• [3] O. Cordn, A historical review of evolutionary learning methods for Mamdani-type fuzzy rule- based systems. International Journal of Approximate Reasoning, Vol. 52, No. 6, 2011, pp.894-913.
• [4] J. S. R. Jang, C. T. Sun, and E. Mizutani, Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Prentice-Hall, Englewood Cliffs, 1997.
• [5] R.E. Precup, and H. Hellendoorn, A survey on industrial applications of fuzzy control, Computers in Industry, Vol. 62, No.3, 2011, pp.213-226.
• [6] O. Cordon, F. Gomide, F. Herrera, F. Hoffmann, and L. Magdalena, Ten years of genetic fuzzy systems: current framework and new trends, Fuzzy Sets & Systems, Vol.141, No.1, 2004, pp. 5-31.
• [7] C. Karr, Genetic algorithms for fuzzy controllers, AI Expert, Vol.6, No. 2, 1991, pp.26-33.
• [8] B. D. Liu, C. Y. Chen, and J. Y. Tsao, Design of adaptive fuzzy logic controller based on linguistic-hedge concepts and genetic algorithms, IEEE Transactions on Systems, Man and Cybernetics, Part B: Vol.31, No.1, 2001, pp.32-53.
• [9] F. Herrera, M. Lozano, and J. L. Verdegay, A learning process for fuzzy control rules using genetic algorithms, Fuzzy Sets and Systems, Vol. 100, No. 1, 1998, pp.143-158.
• [10] T. Pal, and N. R. Pal, SOGARG: A self-organized genetic algorithm-based rule generation scheme for fuzzy controllers. IEEE Transactions on Evolutionary Computation, Vol.7, No.4, 2003, pp.397-415.
• [11] E. Tunstel, and M. Jamshidi, On genetic programming of fuzzy rule-based systems for intelligent control, International Journal of Intelligent Automation and Soft Computing, Vol. 2, No. 3, 1996, pp.271-284.
• [12] A. Tsakonas, Local and global optimization for Takagi–Sugeno fuzzy system by memetic genetic programming, Expert Systems with Applications, Vol.40, No.8, 2013, pp.3282-3298.
• [13] N. Kasabov, and Q. Song, DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction, IEEE Trans. Fuzzy Systems, Vol.10, No. 2, 2002, pp.144-154.
• [14] R. J. Contreras, M.M.B.R. Vellasco, and R. Tanscheit, Hierarchical type-2 neuro-fuzzy BSP model, Information Sciences, Vol. 181, No. 15, 2011, pp. 3210-3224.
• [15] M. P. Hinchliffe, M. J. Willis, H. Hiden, M.T. Tham, B. McKay, and G.W. Barton, Modeling chemical process systems using a multi-gene genetic programming algorithm, In: Proceedings of the First Annual Conference of Genetic Programming, J. R. Koza, MIT Press, Massachussets, 1996, pp. 56-65.
• [16] D. P. Searson, M. J. Willis, and G.A. Montague, Co-evolution of non-linear PLS model components, Journal of Chemometrics, Vol. 2, 2007, pp. 592-603.
• [17] F. Herrera, Genetic fuzzy systems: taxonomy, current research trends and prospects, Evolutionary Intelligence, Vol.1, No.1, 2008, pp.27-46.
• [18] O. Castillo, and P. Melin, A review on the design and optimization of interval type-2 fuzzy controllers, Applied Soft Computing, Vol.12, No.4, 2012, pp.1267-1278.
• [19] M. Fazzolari, R. Alcal, Y. Nojima, H. Ishibuchi, and F. Herrera, A Review of the Application of Multiobjective Evolutionary Fuzzy Systems: Current Status and Further Directions, IEEE Transactions on Fuzzy Sets, Vol.21, No.1, 2013, pp.45-65.
• [20] C. F. Juang, J. Y. Lin, and C. T. Lin, Genetic reinforcement learning through symbiotic evolution for fuzzy controller design, IEEE Transactions on Systems, Man, and Cybernetics, Part B, Vol.30, No.2, 2000, pp.290-302.
• [21] E. De Santis, A. Rizzi, A. Sadeghiany, and F. M. F. Mascioli, Genetic optimization of a fuzzy control system for energy flow management in microgrids, In: Proceedings of IFSA World Congress and NAFIPS Annual Meeting, W. Pedrycz and M. Reformat, IEEE, New Jersey, 2013, pp. 418-423.
• [22] L. H. Hassan, M. Moghavvemi, H. A. Almurib, O. Steinmayer, Application of genetic algorithm in optimization of unified power flow controller parameters and its location in the power system network, International Journal of Electrical Power & Energy Systems, Vol.46, 2013, pp.89-97.
• [23] R. P. Prado, S. Garca-Galn, J. Exposito, and A. J. Yuste, Knowledge acquisition in fuzzy-rule-based systems with particle-swarm optimization, IEEE Transactions on Fuzzy Systems, Vol.18, No.6, 2010, pp.1083-1097.
• [24] O. Castillo, R. Martnez-Marroqun, P. Melin, F. Valdez, and J. Soria, Comparative study of bioinspired algorithms applied to the optimization of type-1 and type-2 fuzzy controllers for an autonomous mobile robot, Information Sciences, Vol.192, 2012, pp.19-38.
• [25] E. Alba, C. Cotta, and J. M. Troya, Typeconstrained genetic programming for rule-base definition in fuzzy logic controllers, In: Proceedings of the First Annual Conference of Genetic Programming, J. R. Koza, MIT Press, Massachussets, 1996, pp. 255-260.
• [26] E. Tunstel, and M. Jamshidi, On genetic programming of fuzzy rule-based systems for intelligent control, International Journal of Intelligent Automation and Soft Computing, Vol.2, No.3, 1996, pp.271-284.
• [27] A. Homaifar, D. Battle, E. Tunstel, and G. Dozier, Genetic Programming Design of Fuzzy Logic Controllers for Mobile Robot Path Tracking, International Journal of Knowledge Based Intelligent Engineering Systems, Vol.4, No.1, 2000, pp.33-52.
• [28] J. R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press, Massachusetts, 1992.
• [29] W. B. Langdon, and R. Poli, Foundations of Genetic Programming, Springer-Verlag, Heidelberg, 2002.
• [30] G. J. Klir, and B. Yuan, Fuzzy sets and fuzzy logic ,Prentice-Hall, New Jersey, 1995.
• [31] T. Calvo, A. Kolesrov, M. Komornkov, and R. Mesiar, Aggregation operators: properties, classes and construction methods, In: Aggregation Operators, T. Calvo et al., Physica-Verlag, Heidelberg, 2002, pp.3-104.
• [32] R. R. Yager, J. Kacprzyk, and G. Beliakov, Recent developments in the ordered weighted averaging operators: theory and practice, Springer, Heidelberg, 2011.
• [33] S. Luke and L. Panait, Lexicographic parsimony pressure, In: Proceedings of the Genetic and Evolutionary Computation Conference,W. B. Langdon et al., Morgan Kaufmann Publishers, New York, 2002, pp. 829-836.
• [34] MATLAB 7.10.0 (R2010a), The MathWorks Inc, Massachusetts, 2010.
• [35] P. R. Thrift, Fuzzy Logic Synthesis with Genetic Algorithms. In: Proceedings of the International Conference on Genetic Algorithms, R. K. Belew and L. B. Booker, Morgan Kauffman Publishers, California, pp. 509-513, July 1991