Yazar "Tkachenko, R." seçeneğine göre listele

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    Hybridization of the SGTM Neural-Like Structure Through Inputs Polynomial Extension
    (Institute of Electrical and Electronics Engineers Inc., 2018) Vitynskyi, P.; Tkachenko, R.; Izonin, I.; Kutucu, H.
    In this paper, a new approach for increasing the approximation accuracy with the use of computational intelligence tools is described. It is based on the compatible use of the neural-like structure of the Successive Geometric Transformations Model and the inputs polynomial extension. To implement such an extension, second degree Wiener polynomial is used. This combination improves the method accuracy for solving various tasks, such as classification and regression, including short-term and long-term prediction, dynamic pricing, as well as image recognition and image scaling, e-commerce. Due to the use of SGTM neural-like structure, the high speed of the system is maintained in both training and using modes. The simulation of the described approach is carried out on real data, the time results of the neural-like structure work and the accuracy results (MAPE, RMSE, R) are given. A comparison of the operation of the method with existing ones, such as Support vector regression, Classic linear SGTM neural-like structure, Linear regression (using Stochastic Gradient Descent), Random Forest, Multilayer Perceptron, AdaBoost are made. The advantages of the developed approach, in particular with regard to the highest accuracy among existing ones were experimentally established. © 2018 IEEE.
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    Non-iterative neural-like predictor for solar energy in Libya
    (CEUR-WS, 2018) Tkachenko, R.; Kutucu, H.; Izonin, I.; Doroshenko, A.; Tsymbal, Y.
    In this paper, a new method for predicting the solar radiation potential in Libya was developed. It is constructed on the basis of the combined use of RBF and non-iterative paradigm of the artificial neural networks construction - the Successive Geometric Transformations Model. This method has the advantages of both approaches - the high prediction accuracy from RBF characteristics and fast non-iterative learning provided by the Successive Geometric Transformations Model. A series of practical experiments were conducted. The training model contained 1440 vectors of the monthly solar radiation, which recorded in 25 Libya's cities from 2010 to 2015. The test model contained 360 data's vectors. Comparison of the proposed method with existing ones is presented. The proposed method showed the best prediction results (MAPE, RMSE) compared to SVM, Linear Regression, the linear Neural-like structure of the Successive Geometric Transformation Model (SGTM), and the RBF based on the NLS SGTM. The proposed approach can be used in different areas, such as e-commerce, material science, images processing and others, especially in Big Data cases. © 2018 CEUR-WS. All rights reserved.