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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
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.
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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.
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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.
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