https://oldena.lpnu.ua/handle/ntb/52529| Title: | Hybridization of the SGTM Neural-like Structure through Inputs Polynomial Extension |
| Authors: | Vitynskyi, Pavlo Tkachenko, Roman Izonin, Ivan Kutucu, Hakan |
| Affiliation: | Lviv Polytechnic National University Karabuk University |
| Bibliographic description (Ukraine): | Hybridization of the SGTM Neural-like Structure through Inputs Polynomial Extension / Pavlo Vitynskyi, Roman Tkachenko, Ivan Izonin, Hakan Kutucu // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Львів : Lviv Politechnic Publishing House, 2018. — P. 386–391. — (Hybrid Systems of Computational Intelligence). |
| Bibliographic description (International): | Hybridization of the SGTM Neural-like Structure through Inputs Polynomial Extension / Pavlo Vitynskyi, Roman Tkachenko, Ivan Izonin, Hakan Kutucu // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Lviv Politechnic Publishing House, 2018. — P. 386–391. — (Hybrid Systems of Computational Intelligence). |
| Is part of: | Data stream mining and processing : proceedings of the IEEE second international conference, 2018 |
| Conference/Event: | IEEE second international conference "Data stream mining and processing" |
| Issue Date: | 28-Feb-2018 |
| Publisher: | Lviv Politechnic Publishing House |
| Place of the edition/event: | Львів |
| Temporal Coverage: | 21-25 August 2018, Lviv |
| Keywords: | approximation Wiener polynomial neural-like structures Successive Geometric Transformation Model input's extension |
| Number of pages: | 6 |
| Page range: | 386-391 |
| Start page: | 386 |
| End page: | 391 |
| Abstract: | 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. |
| URI: | https://ena.lpnu.ua/handle/ntb/52529 |
| ISBN: | © Національний університет „Львівська політехніка“, 2018 © Національний університет „Львівська політехніка“, 2018 |
| Copyright owner: | © Національний університет “Львівська політехніка”, 2018 |
| URL for reference material: | https://doi.org/10.1007/978-3-319-91008-6_58 http://ijarcet.org/wp-content/uploads/IJARCET-VOL-5-ISSUE-4-919-923.pdf |
| References (Ukraine): | [1] O. Mulesa, F. Geche, A. Batyuk, and V. Buchok, “Development of Combined Information Technology for Time Series Prediction,” Advances in Intelligent Systems and Computing II. CSIT 2017. Advances in Intelligent Systems and Computing, vol 689, Springer, Cham, pp. 361-373, 2018. [2] Z. Hu, Y. Bodyanskiy, O. Tyshchenko and O. Boiko, “An Evolving Cascade System Based on a Set of Neo-Fuzzy Nodes,” International Journal of Intelligent Systems and Applications (IJISA), vol.8, no.9, pp.1-7, 2016. [3] I. Dronyuk and O. Fedevych, “Traffic Flows Ateb-Prediction Method with Fluctuation Modeling Using Dirac Functions,” Computer Networks. CN 2017. Communications in Computer and Information Science, vol 718. Springer, Cham, pp. 3-13, 2017. doi.org/10.1007/978-3-319-59767-6_1 [4] V. Teslyuk, V. Beregovskyi, P. Denysyuk, T. Teslyuk and A. Lozynskyi, "Development and Implementation of the Technical Accident Prevention Subsystem for the Smart Home System," International Journal of Intelligent Systems and Applications(IJISA), vol.10, no.1, pp.1-8, 2018. DOI: 10.5815/ijisa.2018.01.01. [5] N. Shakhovska and O. Shamuratov, "The structure of information systems for environmental monitoring," XIth Int. Scientific and Technical Conference Computer Sciences and Information Technologies (CSIT), Lviv, pp. 102-107, 2016. doi: 10.1109/STCCSIT.2016.7589880 [6] I. Pliss and I. Perova “Diagnostic Neuro-Fuzzy System and Its Learning in Medical Data Mining Tasks in Conditions of Uncertainty about Numbers of Attributes and Diagnoses,” Automatic Control and Computer Sciences, vol. 51(6), pp. 391-398, 2017. DOI: 10.3103/S0146411617060062 [7] M. Nazarkevych, R. Oliiarnyk, H. Nazarkevych, O. Kramarenko and I. Onyshschenko “The method of encryption based on Atebfunctions,” In Data Stream Mining and Processing (DSMP), IEEE First International Conference, pp. 129-133, 2016. [8] O. Riznik, I. Yurchak, E. Vdovenko and A. Korchagina, “Model of stegosystem images on the basis of pseudonoise codes,” VIth International Conference on Perspective Technologies and Methods in MEMS Design, Lviv, pp. 51-52, 2010. [9] M. Nazarkevych, R. Oliarnyk, O. Troyan and H. Nazarkevych. “Data protection based on encryption using Ateb-functions,” In Scientific and Technical Conference “Computer Sciences and Information Technologies (CSIT), pp. 30-32, 2016. [10] J. Wainer, Comparison of 14 different families of classification algorithms on 115 binary datasets. arXiv:1606.00930. June 2016. [11] Y. Bodyanskiy, O. Vynokurova, I. Pliss, G. Setlak and P. Mulesa, “Fast learning algorithm for deep evolving GMDH-SVM neural network in data stream mining tasks,” IEEE First International Conference on Data Stream Mining and Processing (DSMP), Lviv, pp. 257- 262. 2016. doi: 10.1109/DSMP.2016.7583555 [12] R. Tkachenko, , I. Izonin, “Model and Principles for the Implementation of Neural-Like Structures based on Geometric Data Transformations,” Advances in Computer Science for Engineering and Education. ICCSEEA2018. Advances in Intelligent Systems and Computing. Springer, Cham, vol.754, pp.578-587, 2019. https://doi.org/10.1007/978-3-319-91008-6_58 [13] R. Tkachenko, P. Tkachenko, I. Izonin and Y. Tsymbal, “LearningBased Image Scaling Using Neural-Like Structure of Geometric Transformation Paradigm”, Advances in Soft Computing and Machine Learning in Image Processing. Studies in Computational Intelligence, Springer, Cham, N. 1, vol. 730, pp. 537-567, 2018. doi.org/10.1007/978-3-319-63754-9_25 [14] U. Polishchuk, P. Tkachenko, R. Tkachenko and I. Yurchak, "Features of the auto-associative neurolike structures of the geometrical transformation machine," 5th Int. Conf. on Perspective Technologies and Methods in MEMS Design, Zakarpattya, Ukrane, pp. 66-67, 2009. [15] I. Tsmots, V. Teslyuk, T. Teslyuk and I. Ihnatyev, “Basic Components of Neuronetworks with Parallel Vertical Group Data Real-Time Processing”, Advances in Intelligent Systems and Computing II. CSIT 2017. Advances in Intelligent Systems and Computing, vol. 689, Springer, Cham, pp. 558 – 576, 2018. [16] O. Riznyk, I. Yurchak and O. Povshuk, “Synthesis of optimal recovery systems in distributed computing using ideal ring bundles”, XII Intern. Conf. on Perspective Technologies and Methods in MEMS Design (MEMSTECH), Lviv, pp. 220-222, 2016. doi:10.1109/MEMSTECH.2016.7507545 [17] R. Tkachenko, Z. Duriagina, I. Lemishka, I. Izonin and A. Trostianchyn “Development of machine learning method of titanium alloys properties identification in additive technologies”, EasternEuropean Journal of Enterprise Technologies, Vol. 3, Iss. 12 (93), 2018, pp. 23-31. DOI: 10.15587/1729-4061.2018.134319 http://ijarcet.org/wp-content/uploads/IJARCET-VOL-5-ISSUE-4-919-923.pdf [18] H. Kutucu and A. Almryad “Modeling of solar energy potential in Libya using an artificial neural network model” In: 2016 IEEE First International Conference on Data Stream Mining & Processing (DSMP), Lviv, 356-359, 2016. [19] Z. Hu, Y. Bodyanskiy and O. Tyshchenko, “A Deep Cascade Neural Network Based on Extended Neo-Fuzzy Neurons and its Adaptive Learning Algorithm,” IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON), Kyiv, Ukraine, pp.801-805, May 29 – June 2, 2017. [20] Y. Bodyanskiy, O. Vynokurova, I. Pliss and D. Peleshko, “Hybrid Adaptive Systems of Computational Intelligence and Their On-line Learning for Green IT in Energy Management Tasks,” Green IT Engineering: Concepts, Models, Complex Systems Architectures. Studies in Systems, Decision and Control, vol 74. Springer, Cham, 2017 [21] Y. Bodyanskiy, G. Setlak, D. Peleshko and O. Vynokurova, “Hybrid generalized additive neuro-fuzzy system and its adaptive learning algorithms,” IEEE 8th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), Warsaw, pp. 328-333, 2015. doi: 10.1109/IDAACS.2015.7340753 [22] Ye. Bodyanskiy, O. Tyshchenko and D. Kopaliani, “Adaptive learning of an evolving cascade neo-fuzzy system in data stream mining tasks,” Evolving Systems, 7(2), pp.107-116, 2016. [23] F. Geche, V. Kotsovsky, A. Batyuk, S. Geche and M. Vashkeba, "Synthesis of Time Series Forecasting Scheme Based on Forecasting Models System," 11th International Conference on ICT in Education, Research and Industrial Applications: Integration, Harmonization and Knowledge Transfer Lviv, Ukraine, May 14-16, CEUR Workshop Proceedings, vol. 1356, pp. 121-136, 2015 [24] I. Perova and Ye. Bodyanskiy, “Fast medical diagnostics using autoassociative neuro-fuzzy memory,” International Journal of Computing, 16 (1), 34-40, 2017. [25] G. Setlak, Y. Bodyanskiy, O. Vynokurova and I. Pliss, "Deep evolving GMDH-SVM-neural network and its learning for Data Mining tasks," 2016 Federated Conference on Computer Science and Information Systems (FedCSIS), Gdansk, pp. 141-145, 2016. |
| References (International): | [1] O. Mulesa, F. Geche, A. Batyuk, and V. Buchok, "Development of Combined Information Technology for Time Series Prediction," Advances in Intelligent Systems and Computing II. CSIT 2017. Advances in Intelligent Systems and Computing, vol 689, Springer, Cham, pp. 361-373, 2018. [2] Z. Hu, Y. Bodyanskiy, O. Tyshchenko and O. Boiko, "An Evolving Cascade System Based on a Set of Neo-Fuzzy Nodes," International Journal of Intelligent Systems and Applications (IJISA), vol.8, no.9, pp.1-7, 2016. [3] I. Dronyuk and O. Fedevych, "Traffic Flows Ateb-Prediction Method with Fluctuation Modeling Using Dirac Functions," Computer Networks. CN 2017. Communications in Computer and Information Science, vol 718. Springer, Cham, pp. 3-13, 2017. doi.org/10.1007/978-3-319-59767-6_1 [4] V. Teslyuk, V. Beregovskyi, P. Denysyuk, T. Teslyuk and A. Lozynskyi, "Development and Implementation of the Technical Accident Prevention Subsystem for the Smart Home System," International Journal of Intelligent Systems and Applications(IJISA), vol.10, no.1, pp.1-8, 2018. DOI: 10.5815/ijisa.2018.01.01. [5] N. Shakhovska and O. Shamuratov, "The structure of information systems for environmental monitoring," XIth Int. Scientific and Technical Conference Computer Sciences and Information Technologies (CSIT), Lviv, pp. 102-107, 2016. doi: 10.1109/STCCSIT.2016.7589880 [6] I. Pliss and I. Perova "Diagnostic Neuro-Fuzzy System and Its Learning in Medical Data Mining Tasks in Conditions of Uncertainty about Numbers of Attributes and Diagnoses," Automatic Control and Computer Sciences, vol. 51(6), pp. 391-398, 2017. DOI: 10.3103/S0146411617060062 [7] M. Nazarkevych, R. Oliiarnyk, H. Nazarkevych, O. Kramarenko and I. Onyshschenko "The method of encryption based on Atebfunctions," In Data Stream Mining and Processing (DSMP), IEEE First International Conference, pp. 129-133, 2016. [8] O. Riznik, I. Yurchak, E. Vdovenko and A. Korchagina, "Model of stegosystem images on the basis of pseudonoise codes," VIth International Conference on Perspective Technologies and Methods in MEMS Design, Lviv, pp. 51-52, 2010. [9] M. Nazarkevych, R. Oliarnyk, O. Troyan and H. Nazarkevych. "Data protection based on encryption using Ateb-functions," In Scientific and Technical Conference "Computer Sciences and Information Technologies (CSIT), pp. 30-32, 2016. [10] J. Wainer, Comparison of 14 different families of classification algorithms on 115 binary datasets. arXiv:1606.00930. June 2016. [11] Y. Bodyanskiy, O. Vynokurova, I. Pliss, G. Setlak and P. Mulesa, "Fast learning algorithm for deep evolving GMDH-SVM neural network in data stream mining tasks," IEEE First International Conference on Data Stream Mining and Processing (DSMP), Lviv, pp. 257- 262. 2016. doi: 10.1109/DSMP.2016.7583555 [12] R. Tkachenko, , I. Izonin, "Model and Principles for the Implementation of Neural-Like Structures based on Geometric Data Transformations," Advances in Computer Science for Engineering and Education. ICCSEEA2018. Advances in Intelligent Systems and Computing. Springer, Cham, vol.754, pp.578-587, 2019. https://doi.org/10.1007/978-3-319-91008-6_58 [13] R. Tkachenko, P. Tkachenko, I. Izonin and Y. Tsymbal, "LearningBased Image Scaling Using Neural-Like Structure of Geometric Transformation Paradigm", Advances in Soft Computing and Machine Learning in Image Processing. Studies in Computational Intelligence, Springer, Cham, N. 1, vol. 730, pp. 537-567, 2018. doi.org/10.1007/978-3-319-63754-9_25 [14] U. Polishchuk, P. Tkachenko, R. Tkachenko and I. Yurchak, "Features of the auto-associative neurolike structures of the geometrical transformation machine," 5th Int. Conf. on Perspective Technologies and Methods in MEMS Design, Zakarpattya, Ukrane, pp. 66-67, 2009. [15] I. Tsmots, V. Teslyuk, T. Teslyuk and I. Ihnatyev, "Basic Components of Neuronetworks with Parallel Vertical Group Data Real-Time Processing", Advances in Intelligent Systems and Computing II. CSIT 2017. Advances in Intelligent Systems and Computing, vol. 689, Springer, Cham, pp. 558 – 576, 2018. [16] O. Riznyk, I. Yurchak and O. Povshuk, "Synthesis of optimal recovery systems in distributed computing using ideal ring bundles", XII Intern. Conf. on Perspective Technologies and Methods in MEMS Design (MEMSTECH), Lviv, pp. 220-222, 2016. doi:10.1109/MEMSTECH.2016.7507545 [17] R. Tkachenko, Z. Duriagina, I. Lemishka, I. Izonin and A. Trostianchyn "Development of machine learning method of titanium alloys properties identification in additive technologies", EasternEuropean Journal of Enterprise Technologies, Vol. 3, Iss. 12 (93), 2018, pp. 23-31. DOI: 10.15587/1729-4061.2018.134319 http://ijarcet.org/wp-content/uploads/IJARCET-VOL-5-ISSUE-4-919-923.pdf [18] H. Kutucu and A. Almryad "Modeling of solar energy potential in Libya using an artificial neural network model" In: 2016 IEEE First International Conference on Data Stream Mining & Processing (DSMP), Lviv, 356-359, 2016. [19] Z. Hu, Y. Bodyanskiy and O. Tyshchenko, "A Deep Cascade Neural Network Based on Extended Neo-Fuzzy Neurons and its Adaptive Learning Algorithm," IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON), Kyiv, Ukraine, pp.801-805, May 29 – June 2, 2017. [20] Y. Bodyanskiy, O. Vynokurova, I. Pliss and D. Peleshko, "Hybrid Adaptive Systems of Computational Intelligence and Their On-line Learning for Green IT in Energy Management Tasks," Green IT Engineering: Concepts, Models, Complex Systems Architectures. Studies in Systems, Decision and Control, vol 74. Springer, Cham, 2017 [21] Y. Bodyanskiy, G. Setlak, D. Peleshko and O. Vynokurova, "Hybrid generalized additive neuro-fuzzy system and its adaptive learning algorithms," IEEE 8th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), Warsaw, pp. 328-333, 2015. doi: 10.1109/IDAACS.2015.7340753 [22] Ye. Bodyanskiy, O. Tyshchenko and D. Kopaliani, "Adaptive learning of an evolving cascade neo-fuzzy system in data stream mining tasks," Evolving Systems, 7(2), pp.107-116, 2016. [23] F. Geche, V. Kotsovsky, A. Batyuk, S. Geche and M. Vashkeba, "Synthesis of Time Series Forecasting Scheme Based on Forecasting Models System," 11th International Conference on ICT in Education, Research and Industrial Applications: Integration, Harmonization and Knowledge Transfer Lviv, Ukraine, May 14-16, CEUR Workshop Proceedings, vol. 1356, pp. 121-136, 2015 [24] I. Perova and Ye. Bodyanskiy, "Fast medical diagnostics using autoassociative neuro-fuzzy memory," International Journal of Computing, 16 (1), 34-40, 2017. [25] G. Setlak, Y. Bodyanskiy, O. Vynokurova and I. Pliss, "Deep evolving GMDH-SVM-neural network and its learning for Data Mining tasks," 2016 Federated Conference on Computer Science and Information Systems (FedCSIS), Gdansk, pp. 141-145, 2016. |
| Content type: | Conference Abstract |
| Appears in Collections: | Data stream mining and processing : proceedings of the IEEE second international conference |
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2018_Vitynskyi_P-Hybridization_of_the_386-391.pdf | 312.24 kB | Adobe PDF | View/Open | |
| 2018_Vitynskyi_P-Hybridization_of_the_386-391__COVER.png | 559.96 kB | image/png | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.