https://oldena.lpnu.ua/handle/ntb/52480| Title: | About Kernel Structure Construction of the Generalized Neural Functions |
| Authors: | Geche, Fedir Mulesa, Oksana Voloshchuk, Veronika Batyuk, Anatoliy |
| Affiliation: | Uzhhorod National University Lviv Polytechnic National University |
| Bibliographic description (Ukraine): | About Kernel Structure Construction of the Generalized Neural Functions / Fedir Geche, Oksana Mulesa, Veronika Voloshchuk, Anatoliy Batyuk // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Львів : Lviv Politechnic Publishing House, 2018. — P. 151–156. — (Dynamic Data Mining & Data Stream Mining). |
| Bibliographic description (International): | About Kernel Structure Construction of the Generalized Neural Functions / Fedir Geche, Oksana Mulesa, Veronika Voloshchuk, Anatoliy Batyuk // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Lviv Politechnic Publishing House, 2018. — P. 151–156. — (Dynamic Data Mining & Data Stream Mining). |
| 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: | spectrum of function generalized neural element structure vector kernel of function convex hull character of group synthesis metric matrix of tolerance |
| Number of pages: | 6 |
| Page range: | 151-156 |
| Start page: | 151 |
| End page: | 156 |
| Abstract: | The paper introduces concept of a modified kernel of the Boolean functions. Applying such a concept, the criteria for the implementation of the Boolean functions by one generalized neural element are obtained. The effective and necessary conditions to check whether the algebra of logic functions belong to the class of the generalized neural functions are given. A sufficient condition for the implementation of the Boolean functions is obtained by one generalized neural element on the basis of which it is possible to develop effective methods for the synthesis of the generalized integer neural elements with a large number of inputs. |
| URI: | https://ena.lpnu.ua/handle/ntb/52480 |
| ISBN: | © Національний університет „Львівська політехніка“, 2018 © Національний університет „Львівська політехніка“, 2018 |
| Copyright owner: | © Національний університет “Львівська політехніка”, 2018 |
| References (Ukraine): | [1] M. Azarbad, S. Hakimi, and A. Ebrahimzadeh, “Automatic Recognition of Digital Communication Signal,” International Journal of Energy, Information and Communications vol.3, is. 4, pp. 21–33, 2012. [2] I. V. Isonin, R. O. Tkachenko, D. D. Peleshko, and D. A. Batuk, “Neural network method for changing the resolution of images,” Systems of information processing, is. 9(134 pp. 30–34, 2015. [3] F. Amato, J. L. González-Hernández, and J. Havel, “Artifical neural networks combined with experimental desing: a “soft” approach for chemical kinetics,” Talanta, vol. 93, pp. 72–78. 2012. [4] F. Geche, O. Mulesa, S. Geche, and M. Vashkeba, “Development of the method of synthesis of the prediction scheme on the basis of basic forecasting models.” Technological audit and production reserves, no. 3/2(23), pp. 36–41, 2015. Mode of access : DOI : 10.15587/2312-8372.2015.44932. [5] P. Dey, A. Lamba, S. Kumary, and N. Marwaha, “Application of an artifical neural network in the prognosis of chronic myeloid leukemia,” Analytical and quantitative cytology and histology, International Academy of Cytology and American Society of Cytology, vol. 33 (6), pp. 335–339, 2011. [6] A. S. Liu, and Q. Zhu, “Automatic modulation classification based on the combination of clustering and neural network,” The Journal of China Universities of Ports and Telecommunication, vol. 18, no.4, pp. 13–19, 2011. [7] A. Pathok, and A. K. Wadhwani,. “Data Compression of ECG Signals Using Error Back Propagation (EBP) Algorithm,” International Journal of Engineering and Advence Technology (IJEAT), vol. 1, iss. 4, pp. 256–260, 2012. [8] Ye. Bodyansky, P. Grimm, S. Mashtalir, and V. Vinarski, “Fast training of neural networks for image compression,” Lecture Notes in Computer Science. Berlin-Heidelberg-New York, Springer, vol. 6171, pp. 165–173, 2010. [9] Ye. Bodyanskiy I. Pliss., D. Peleshko, Yu. Rashkevych, and O. Vynokurova, “Hybrid Generalized Additive Wavelet-NeuroFuzzy-System and its Adaptive Learning,” Dependability Engineering and Complex Systems: Proceedings of the Eleventh International Conference on Dependability and Complex Systems DepCoSRELCOMEX., Brunow, Poland, pp.51-61, June 27-July 1, 2016 [10] N. V. Shovgun, “Analysis of the effectiveness of fuzzy neural networks concerning the credit risk assessment,” Information technologies & knowledge. ITHEA IBS ISC, vol.7, pp. 286–293, 2013. [11] Y .Bodyanskiy, G. Setlak, D. Peleshko, and O. Vynokurova, “Hybrid Generalized Additive Neuro-Fuzzy System and its Adaptive Learning Algorithms,” 8th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, Warsaw, Poland, pp. 328-333, 24-26 September 2015 [12] T.Teslyuk, I. Tsmots., V. Teslyuk, M. Medykovskyy, and Y. Opotyak, “Architecture and Models for System-Level ComputerAided Design of the Management System of Energy Efficiency of Technological Processes at the Enterprise,” Advances in Intelligent Systems and Computing, vol. 689, Springer, Cham. pp. 538 – 557, 2018. [13] 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, vol. 689, Springer, Cham. pp. 558 – 576, 2018. [14] Vasyl Teslyuk, Vasyl Beregovskyi, Pavlo Denysyuk, Taras Teslyuk, and Andrii 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. [15] C. Curtis, I. Reiner, Representation theory of finite groups and associative algebras. M .: Nauka, 1969. [16] B. I. Golubov, A. V. Efimov, and V. A. Skvortsov, Walsh series and transformations. Theory and applications. M.: Nauka, 1987. [17] F. Geche, O. Mulesa, and V. Buchok, “Synthesis of generalized neural elements by means of the tolerance matrices,” Eastern European Journal of Advanced Technology, vol. 4 / 4(88), pp.50-62, 2017. [18] N.N. Aisenberg, A.A. Bovdi, E.J. Gergo, and F.E. Geche, “Some algebraic aspects of threshold logic,” Cybernetics, no. 2, pp. 26-30, 1980. |
| References (International): | [1] M. Azarbad, S. Hakimi, and A. Ebrahimzadeh, "Automatic Recognition of Digital Communication Signal," International Journal of Energy, Information and Communications vol.3, is. 4, pp. 21–33, 2012. [2] I. V. Isonin, R. O. Tkachenko, D. D. Peleshko, and D. A. Batuk, "Neural network method for changing the resolution of images," Systems of information processing, is. 9(134 pp. 30–34, 2015. [3] F. Amato, J. L. González-Hernández, and J. Havel, "Artifical neural networks combined with experimental desing: a "soft" approach for chemical kinetics," Talanta, vol. 93, pp. 72–78. 2012. [4] F. Geche, O. Mulesa, S. Geche, and M. Vashkeba, "Development of the method of synthesis of the prediction scheme on the basis of basic forecasting models." Technological audit and production reserves, no. 3/2(23), pp. 36–41, 2015. Mode of access : DOI : 10.15587/2312-8372.2015.44932. [5] P. Dey, A. Lamba, S. Kumary, and N. Marwaha, "Application of an artifical neural network in the prognosis of chronic myeloid leukemia," Analytical and quantitative cytology and histology, International Academy of Cytology and American Society of Cytology, vol. 33 (6), pp. 335–339, 2011. [6] A. S. Liu, and Q. Zhu, "Automatic modulation classification based on the combination of clustering and neural network," The Journal of China Universities of Ports and Telecommunication, vol. 18, no.4, pp. 13–19, 2011. [7] A. Pathok, and A. K. Wadhwani,. "Data Compression of ECG Signals Using Error Back Propagation (EBP) Algorithm," International Journal of Engineering and Advence Technology (IJEAT), vol. 1, iss. 4, pp. 256–260, 2012. [8] Ye. Bodyansky, P. Grimm, S. Mashtalir, and V. Vinarski, "Fast training of neural networks for image compression," Lecture Notes in Computer Science. Berlin-Heidelberg-New York, Springer, vol. 6171, pp. 165–173, 2010. [9] Ye. Bodyanskiy I. Pliss., D. Peleshko, Yu. Rashkevych, and O. Vynokurova, "Hybrid Generalized Additive Wavelet-NeuroFuzzy-System and its Adaptive Learning," Dependability Engineering and Complex Systems: Proceedings of the Eleventh International Conference on Dependability and Complex Systems DepCoSRELCOMEX., Brunow, Poland, pp.51-61, June 27-July 1, 2016 [10] N. V. Shovgun, "Analysis of the effectiveness of fuzzy neural networks concerning the credit risk assessment," Information technologies & knowledge. ITHEA IBS ISC, vol.7, pp. 286–293, 2013. [11] Y .Bodyanskiy, G. Setlak, D. Peleshko, and O. Vynokurova, "Hybrid Generalized Additive Neuro-Fuzzy System and its Adaptive Learning Algorithms," 8th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, Warsaw, Poland, pp. 328-333, 24-26 September 2015 [12] T.Teslyuk, I. Tsmots., V. Teslyuk, M. Medykovskyy, and Y. Opotyak, "Architecture and Models for System-Level ComputerAided Design of the Management System of Energy Efficiency of Technological Processes at the Enterprise," Advances in Intelligent Systems and Computing, vol. 689, Springer, Cham. pp. 538 – 557, 2018. [13] 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, vol. 689, Springer, Cham. pp. 558 – 576, 2018. [14] Vasyl Teslyuk, Vasyl Beregovskyi, Pavlo Denysyuk, Taras Teslyuk, and Andrii 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. [15] C. Curtis, I. Reiner, Representation theory of finite groups and associative algebras. M ., Nauka, 1969. [16] B. I. Golubov, A. V. Efimov, and V. A. Skvortsov, Walsh series and transformations. Theory and applications. M., Nauka, 1987. [17] F. Geche, O. Mulesa, and V. Buchok, "Synthesis of generalized neural elements by means of the tolerance matrices," Eastern European Journal of Advanced Technology, vol. 4, 4(88), pp.50-62, 2017. [18] N.N. Aisenberg, A.A. Bovdi, E.J. Gergo, and F.E. Geche, "Some algebraic aspects of threshold logic," Cybernetics, no. 2, pp. 26-30, 1980. |
| Content type: | Conference Abstract |
| Appears in Collections: | Data stream mining and processing : proceedings of the IEEE second international conference |
| File | Description | Size | Format | |
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| 2018_Geche_F-About_Kernel_Structure_Construction_151-156.pdf | 557.93 kB | Adobe PDF | View/Open | |
| 2018_Geche_F-About_Kernel_Structure_Construction_151-156__COVER.png | 480.27 kB | image/png | View/Open |
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