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Документ Відкритий доступ Clusterization of vector and matrix data arrays using the combined evolutionary method of fish schools(КПІ ім. Ігоря Сікорського, 2022) Bodyanskiy, Ye.; Shafronenko, А.; Pliss, І.The problem of clustering data arrays described in both vector and matrix forms and based on the optimization of data distribution density functions in these arrays is considered. For the optimization of these functions, the algorithm that is a hybrid of Fish School Search, random search, and evolutionary optimization is proposed. This algorithm does not require calculating the optimized function’s derivatives and, in the general case, is designed to find optimums of multiextremal functions of the matrix argument (images). The proposed approach reduces the number of runs of the optimization procedure, finds extrema of complex functions with many extrema, and is simple in numerical implementation.Документ Відкритий доступ Hybrid system of computational intelligence based on bagging and group method of data handling(КПІ ім. Ігоря Сікорського, 2024) Bodyanskiy, Ye.; Kuzmenko, O.; Zaichenko, He.; Zaychenko, Yu.The paper considers the problem of short- and middle-term forecasting in the financial sphere. To solve this problem, a hybrid system of computational intelligence based on the group method of data handling (GMDH) and bagging, as well as an algorithm for its training, is proposed. The odd stacks of the hybrid system are formed by ensembles of parallel connected subsystems. ARIMA and the GMDHneo-fuzzy hybrid network were chosen as such subsystems. The proposed system does not require a large training data set, automatically determines the number of stacks during training, and provides online operation. The experimental investigations were conducted using the proposed hybrid system, as well as separately using ARIMA and GMDH-neo-fuzzy. The accuracy of the predictions obtained is compared, based on which the feasibility of using the proposed hybrid system is substantiated.Документ Відкритий доступ Multilayer gmdh-neuro-fuzzy network based on extended neo-fuzzy neurons and its application in online facial expression recognition(КПІ ім. Ігоря Сікорського, 2020) Bodyanskiy, Ye.; Zaychenko, Yu.; Hamidov, G.; Kulishova, N.Документ Відкритий доступ Вейвлет-нейро-фаззі-система типу-2 та алгоритм її навчання в задачах інтелектуальної обробки інформації(НТУУ "КПІ", 2010) Бодянский, Є. В.; Винокурова, О. А.; Bodyanskiy, Ye.; Vynokurova, O. A.; Бодянський, Е. В.; Винокурова, Е. А.