图书馆订阅: Guest
Begell Digital Portal Begell 数字图书馆 电子图书 期刊 参考文献及会议录 研究收集
自动化与信息科学期刊
SJR: 0.275 SNIP: 0.59 CiteScore™: 0.8

ISSN 打印: 1064-2315
ISSN 在线: 2163-9337

卷:
卷 52, 2020 卷 51, 2019 卷 50, 2018 卷 49, 2017 卷 48, 2016 卷 47, 2015 卷 46, 2014 卷 45, 2013 卷 44, 2012 卷 43, 2011 卷 42, 2010 卷 41, 2009 卷 40, 2008 卷 39, 2007 卷 38, 2006 卷 37, 2005 卷 36, 2004 卷 35, 2003 卷 34, 2002 卷 33, 2001 卷 32, 2000 卷 31, 1999 卷 30, 1998 卷 29, 1997 卷 28, 1996

自动化与信息科学期刊

DOI: 10.1615/JAutomatInfScien.v51.i3.20
pages 4-25

Generation of Rule Bases of Fuzzy Systems Based on Modified Ant Colony Algorithms

Yuriy P. Kondratenko
Petro Mohyla Black Sea State University, Nikolaev, Ukraine
Alexey V. Kozlov
Admiral Makarov National University of Shipbuilding, Nikolaev; Petro Mohyla Black Sea National University, Nikolaev

ABSTRACT

This article is dedicated to the development and study of the method of generation of fuzzy systems rule bases of Mamdani-type with the formation of optimal consequents based on modified ant colony optimization algorithms. The obtained method makes it possible to effectively generate the rule bases with the optimal consequents for the Mamdani-type fuzzy systems in the following cases: at insufficient amount of initial information (under conditions of a high degree of information uncertainty); at a sufficiently large number of rules for which the development of a rule base based on the knowledge of experts is not always effective; at different levels of experts qualification. The study of the effectiveness of the proposed method in this work is carried out at the development of the rule base of the Mamdani-type fuzzy controller for the automatic control system of the reactor temperature of the specialized pyrolysis complex, that is intended for utilization of polymer waste. At the same time, the generation of the consequents of the rule base of this fuzzy controller was carried out with various adjustable parameters of the method, such as the total number of agents and the number of elite agents in the population. Based on the analysis of the results of the conducted experiments, the optimal configuration of the population of agents of the method was determined, for which the optimal vector of the consequents of the rule base can be found at the lowest computational and time costs. Also, the results of computer simulations of transients showed that the automatic temperature control system with the developed fuzzy controller rule base based on the proposed by the authors method with the optimal configuration of parameters has a significantly lower value of the objective function and higher control quality parameters than the system with the rule base on the basis of experts knowledge, which, in turn, confirms the high efficiency of this method.

REFERENCES

  1. Zadeh L.A., Abbasov A.M., Yager R.R., Shahbazova S.N., Reformat M.Z., Eds., Recent developments and new directions in soft computing, STUDFUZ 317, Springer, Cham, 2014, DOI: 10.1007/978-3-319-06323-2.

  2. Hampel R., Wagenknecht M., Chaker N., Fuzzy control: Theory and practice, Physika-Verlag, Heidelberg, New York, 2000, DOI: 10.1007/978-3-7908-1841-3.

  3. Kosko B., Fuzzy systems as universal approximators, IEEE Trans. on Computers, 1994, 43, No. 11, 1329-1333, DOI: 10.1109/12.324566.

  4. Rotshtein A.P., Intellectual identification technologies: fuzzy sets, genetic algorithms, neural networks [in Russian], UNIVERSUM-Vinnitsa,Vinnitsa, 1999.

  5. Takagi T., Sugeno M., Fuzzy identification of systems and its applications to modeling and control, IEEE Transactions on Systems, Man, and Cybernetics, 1985, SMC-15, No. 1, 116-132, DOI: 10.1109/TSMC.1985.6313399.

  6. Jamshidi M., Kreinovich V., Kacprzyk J, Eds., Advance trends in soft computing, Springer-Verlag, Cham, 2013, DOI: https://doi.org/10.l007/978-3-319-03674-8.

  7. Zadeh L.A., The role of fuzzy logic in modeling, identification and control, Modeling Identification and Control, 1994, 15(3), 191-203, DOI: https://doi.org/10.1142/9789814261302_0041.

  8. Piegat A., Fuzzy modeling and control, Physica, 2013, 69, DOI: 10.1007/978-3-7908-1824-6.

  9. Rutkovskaya D., Pilinsky M., Rutkovsky L., Neural networks, genetic algorithms and fuzzy systems [in Russian], Goryachaya liniya, Telekom, Moscow, 2006.

  10. Yager R.R., Filev D.P., Essentials of fuzzy modeling and control, Sigart Bulletin, 1994, 6, No. 4, 22-23, DOI: https://doi.org/10.1002/(SICI)1097-4571(199512)46:10<791::AID-ASI12>3.0.CO;2-H.

  11. Kondratenko Y.P., Simon D., Structural and parametric optimization of fuzzy control and decision making systems. Recent developments and the new direction in soft-computing foundations and applications, Selected Papers from the 6th World Conference on Soft Computing, Berkeley, USA, 2016, Series: Studies in Fuzziness and Soft Computing, 2018, 361, Springer International Publishing, 273-289, DOI: https://doi.org/10.1007/978-3-319-75408-6_22.

  12. Lodwick W.A., Kacprzhyk J., Fuzzy optimization., Eds., STUDFUZ 254, Springer-Verlag, Berlin, Heidelberg, 2010, DOI: 10.1007/978-3-642-13935-2.

  13. Kondratenko Y.P., Al Zubi E.Y.M., The optimization approach for increasing efficiency of digital fuzzy controllers, Annals of DAAAM for 2009 & Proceeding of the 20th Int. DAAAM Symp. "Intelligent Manufacturing and Automation ", Published by DAAAM International, Vienna, Austria, 2009, 1589-1591.

  14. Kondratenko Y.P., Altameem T.A., Al Zubi E.Y.M., The optimization of digital controllers for fuzzy systems design, Advances in Modelling and Analysis, 2010, Ser. A, 47, 19-29.

  15. Simon D., H&#8734; estimation for fuzzy membership function optimization, International Journal of Approximate Reasoning, 2005, 40, 224-242, DOI: https://doi.org/10.1016/j.ijar.2005.04.002.

  16. Simon D., Evolutionary optimization algorithms: biologically inspired and population-based approaches to computer intelligence, John Wiley & Sons, 2013, ISBN: 978-0-470-93741-9.

  17. Kondratenko Y.P., Klymenko L.P., Al Zubi E.Y.M., Structural optimization of fuzzy systems' rules base and aggregation models, Kybernetes, 2013, 42, No. 5, 831-843, DOI: https://doi.org/10.1108/ K-03-2013-0053.

  18. Ishibuchi H., Yamamoto T., Fuzzy rule selection by multi-objective genetic local search ,algorithms and rule evaluation measures in data mining, Fuzzy Sets and Systems, 2004, 141, No. 1, 59-88, DOI: https://doi.org/10.1016/S0165-0114(03)00114-3.

  19. Subbotin S.O, Oleynik A.O., Oleynik O.O., Non-iterative, evolutionary and multiagent methods for the synthesis of fuzzy and neural network models [in Ukrainian], ZNTU, Zaporozhye, 2009.

  20. Haupt R., Haupt S., Practical genetic algorithms, John Wiley & Sons, New Jersey, 2004.

  21. Quijano N., Passino K.M., Honey bee social foraging algorithms for resource allocation: theory and application, Publishing house of the Ohio State University, Columbus, 2007, DOI: https://doi.org/ 10.1016/j.engappai.2010.05.004.

  22. Kim D.H., Cho C.H., Bacterial foraging based neural network fuzzy learning, Proceedings of the 2nd Indian International Conference on Artificial Intelligence (IICAI 2005), IICAI, Pune, 2005, 2030-2036.

  23. Engelbrecht A., A study of particle swarm optimization particle trajectories, A. EngelbrechtInformation Sciences, 2006, No. 176(8), 937-971, DOI: https://doi.org/10.1016/j.ins.2005.02.003.

  24. Kondratenko Y.P. , Kozlov O.V., Korobko O.V., Two modifications of the automatic rule base synthesis for fuzzy control and decision making systems, Chapter in a book: "Information processing and management of uncertainty in knowledge-based systems. Theory and foundations", Book Series: Communications in Computer and Information Science, Ed. by Medina J., Ojeda-Aciego M., Verdegay J.L., Pelta D.A., Cabrera I.P., Bouchon-Meunier B., Yager R.R., 854, Springer International Publishing, Berlin, Heidelberg, 2018, 570-582, DOI: https://doi.org/10.1007/978-3-319-91476-3_47.

  25. Oleinik Al.A., Comparative analysis of optimization methods based on the ant colony method, Kompyuternoye modelirovaniye i intellektualnyye sistemy, ZNTU, Zaporozhye, 2007.

  26. Gan R., Guo Q., Chang H., Yi Y., Improved ant colony optimization algorithm for the traveling salesman problems, Journal of Systems Engineering and Electronics, 2010, 329-333, DOI: 10.3969/j.issn.1004-4132.2010.02.025.

  27. Chen R.-M., Shen Y.-M., Wang C.-T., Ant colony optimization inspired swarm optimization for grid task scheduling, International Symposium on Computer, Consumer and Control (IS3C), 2016, 461.

  28. Chengming Q., Vehicle routing optimization in logistics distribution using hybrid ant colony algorithm TELKOMNIKA, Indonesian Journal of Electrical Engineering, 2013, 11, No. 9, 5308.

  29. Dorigo M., Birattari M., Ant colony optimization. Encyclopedia of machine learning, Ed. by Sammut C., Webb G.I., Springer, Boston, MA, DOI: https://doi.org/10.1007/978-1-4899-7687-1.

  30. Benhala B., Ahaitouf A., Fakhfakh M., Mechaqrane A., New adaptation of the ACO algorithm for the analog circuits design optimization, International Journal of Computer Science (IJCSI), 2012, 9, No. 3, 360-367.

  31. Khaluf Y., Gullipalli S., An efficient ant colony system for edge detection in image processing, Proceedings of the European Conference on Artificial Life, 2015, 398-405, DOI: http://dx.doi.org/ 10.7551/978-0-262-33027-5-ch071.

  32. Kozlov O., Kondratenko G., Gomolka Z., Kondratenko Y., Synthesis and optimization of green fuzzy controllers for the reactors of the specialized pyrolysis plants, Chapter in a book "Green IT Engineering: Social, business and industrial applications", Book Series: Studies in Systems, Decision and Control, Ed. by Kharchenko V., Kondratenko Y. Kacprzyk J., 171, Springer, Cham, 2018, 373-396. DOI: 10.1007/ 978-3-030-00253-4_16.

  33. Kondratenko Y.P., Kozlov O.V., Mathematic modelling of reactor's temperature mode of multiloop pyrolysis plant, Lecture Notes in Business Information Processing: Modelling and Simulation in Engineering, Economics and Management, 2012, 115, Springer-Verlag, Berlin, Heidelberg, 178-187, DOI: https://doi.org/10.1007/978-3-642-30433-0_18.

  34. Kondratenko Y.P., Kozlov O.V., Kondratenko G.V., Atamanyuk L.P., Mathematical model and parametrical identification of ecopyrogenesis plant based on soft computing techniques, Chapter in a book "Complex systems: Solutions and challenges in economics, management and engineering", Book Series: Studies in Systems, Decision and Control, Ed. by Christian Berger-Vachon, Anna Maria Gil Lafuente, Janusz Kacprzyk, Yuriy Kondratenko, Jose M. Merigo, Carlo Francesco Morabito, 125, Springer International Publishing, Berlin, Heidelberg, 2018, 201-233, DOI: https://doi.org/ 10.1007/978-3-319-69989-9 13.


Articles with similar content:

Bank − Complex System
Journal of Automation and Information Sciences, Vol.51, 2019, issue 8
Marina V. Timoshenko , Valeriy A. Velichkin
Parametric study of a low-temperature differential Stirling engine for low-grade thermal energy recovery
Proceedings of the 25th National and 3rd International ISHMT-ASTFE Heat and Mass Transfer Conference (IHMTC-2019), Vol.0, 2019, issue
Naveen Kumar, Siddharth Ramachandran, Mallina Venkata Timmaraju
Suboptimal Control: HL-algorithm for Solution of Stabilization Problem
Journal of Automation and Information Sciences, Vol.28, 1996, issue 3-4
V. N. Afanasiev, A. N. Danilina, C. C. Gracheva
THE METHOD OF OPTIMAL PROFILING OF FLYING VEHICLE FUSELAGE AND NOZZLE AT THE CRUISING SUPERSONIC FLIGHT
TsAGI Science Journal, Vol.44, 2013, issue 3
Sergey Alexandrovich Takovitskii, Anatoliy Pavlovich Mazurov
MATHEMATICAL MODELS OF MULTISERVICE (SELF-SIMILAR) TRAFFIC GENERATION AND SERVICING PROCESSES
Telecommunications and Radio Engineering, Vol.70, 2011, issue 11
K. I. Sychev