Development of a method for training artificial neural networks for intelligent decision support systems
DOI:
https://doi.org/10.15587/1729-4061.2020.203301Keywords:
artificial neural networks, information processing, intelligent decision support systemsAbstract
A method for training artificial neural networks for intelligent decision support systems has been developed. The method provides training not only of the synaptic weights of the artificial neural network, but also the type and parameters of the membership function, architecture and parameters of an individual network node. The architecture of artificial neural networks is trained if it is not possible to ensure the specified quality of functioning of artificial neural networks due to the training of parameters of an artificial neural network. The choice of architecture, type and parameters of the membership function takes into account the computing resources of the tool and the type and amount of information received at the input of the artificial neural network. The specified method allows the training of an individual network node and the combination of network nodes. The development of the proposed method is due to the need for training artificial neural networks for intelligent decision support systems, in order to process more information, with unambiguous decisions being made. This training method provides on average 10–18 % higher learning efficiency of artificial neural networks and does not accumulate errors during training. The specified method will allow training artificial neural networks, identifying effective measures to improve the functioning of artificial neural networks, increasing the efficiency of artificial neural networks through training the parameters and architecture of artificial neural networks. The method will allow reducing the use of computing resources of decision support systems, developing measures aimed at improving the efficiency of training artificial neural networks and increasing the efficiency of information processing in artificial neural networksReferences
- Kalantaievska, S., Pievtsov, H., Kuvshynov, O., Shyshatskyi, A., Yarosh, S., Gatsenko, S. et. al. (2018). Method of integral estimation of channel state in the multiantenna radio communication systems. Eastern-European Journal of Enterprise Technologies, 5 (9 (95)), 60–76. doi: https://doi.org/10.15587/1729-4061.2018.144085
- Kuchuk, N., Mohammed, A. S., Shyshatskyi, A., Nalapko, O. (2019). The method of improving the efficiency of routes selection in networks of connection with the possibility of self-organization. International Journal of Advanced Trends in Computer Science and Engineering, 8 (1.2), 1–6, doi: https://doi.org/10.30534/ijatcse/2019/0181.22019
- Zhang, J., Ding, W. (2017). Prediction of Air Pollutants Concentration Based on an Extreme Learning Machine: The Case of Hong Kong. International Journal of Environmental Research and Public Health, 14 (2), 114. doi: https://doi.org/10.3390/ijerph14020114
- Katranzhy, L., Podskrebko, O., Krasko, V. (2018). Modelling the dynamics of the adequacy of bank’s regulatory capital. Baltic Journal of Economic Studies, 4 (1), 188–194. doi: https://doi.org/10.30525/2256-0742/2018-4-1-188-194
- Manea, E., Di Carlo, D., Depellegrin, D., Agardy, T., Gissi, E. (2019). Multidimensional assessment of supporting ecosystem services for marine spatial planning of the Adriatic Sea. Ecological Indicators, 101, 821–837. doi: https://doi.org/10.1016/j.ecolind.2018.12.017
- Çavdar, A. B., Ferhatosmanoğlu, N. (2018). Airline customer lifetime value estimation using data analytics supported by social network information. Journal of Air Transport Management, 67, 19–33. doi: https://doi.org/10.1016/j.jairtraman.2017.10.007
- Kachayeva, G. I., Mustafayev, A. G. (2018). The use of neural networks for the automatic analysis of electrocardiograms in diagnosis of cardiovascular diseases. Herald of Dagestan State Technical University. Technical Sciences, 45 (2), 114–124. doi: https://doi.org/10.21822/2073-6185-2018-45-2-114-124
- Zhdanov, V. V. (2016). Experimental method to predict avalanches based on neural networks. Ice and Snow, 56 (4), 502–510. doi: https://doi.org/10.15356/2076-6734-2016-4-502-510
- Kanev, A., Nasteka, A., Bessonova, C., Nevmerzhitsky, D., Silaev, A., Efremov, A., Nikiforova, K. (2017). Anomaly detection in wireless sensor network of the “smart home” system. 2017 20th Conference of Open Innovations Association (FRUCT), 118–124. doi: https://doi.org/10.23919/fruct.2017.8071301
- Sreeshakthy, M., Preethi, J. (2016). Classification of human emotion from deap EEG signal using hybrid improved neural networks with Сuckoo search. BRAIN. Broad Research in Artificial Intelligence and Neuroscience, 6 (3-4), 60–73. Available at: https://www.slideshare.net/bpatrut/classification-of-human-emotion-from-deap-eeg-signal-using-hybrid-improved-neural-networks-with-cuckoo-search
- Chica, J., Zaputt, S., Encalada, J., Salamea, C., Montalvo, M. (2019). Objective assessment of skin repigmentation using a multilayer perceptron. Journal of Medical Signals & Sensors, 9 (2), 88. doi: https://doi.org/10.4103/jmss.jmss_52_18
- Massel, L. V., Gerget, O. M., Massel, A. G., Mamedov, T. G. (2019). The Use of Machine Learning in Situational Management in Relation to the Tasks of the Power Industry. EPJ Web of Conferences, 217, 01010. doi: https://doi.org/10.1051/epjconf/201921701010
- Abaci, K., Yamacli, V. (2019). Hybrid Artificial Neural Network by Using Differential Search Algorithm for Solving Power Flow Problem. Advances in Electrical and Computer Engineering, 19 (4), 57–64. doi: https://doi.org/10.4316/aece.2019.04007
- Mishchuk, O. S., Vitynskyi, P. B. (2018). Neural Network with Combined Approximation of the Surface of the Response. Research Bulletin of the National Technical University of Ukraine “Kyiv Politechnic Institute”, 2, 18–24. doi: https://doi.org/10.20535/1810-0546.2018.2.129022
- Kazemi, M., Faezirad, M. (2018). Efficiency estimation using nonlinear influences of time lags in DEA Using Artificial Neural Networks. Industrial Management Journal, 10 (1), 17–34. doi: https://doi.org/10.22059/imj.2018.129192.1006898
- Parapuram, G., Mokhtari, M., Ben Hmida, J. (2018). An Artificially Intelligent Technique to Generate Synthetic Geomechanical Well Logs for the Bakken Formation. Energies, 11 (3), 680. doi: https://doi.org/10.3390/en11030680
- Prokoptsev, N. G., Alekseenko, A. E., Kholodov, Y. A. (2018). Traffic flow speed prediction on transportation graph with convolutional neural networks. Computer Research and Modeling, 10 (3), 359–367. doi: https://doi.org/10.20537/2076-7633-2018-10-3-359-367
- Bodyanskiy, Y., Pliss, I., Vynokurova, O. (2013). Flexible Neo-fuzzy Neuron and Neuro-fuzzy Network for Monitoring Time Series Properties. Information Technology and Management Science, 16 (1), 47–52. doi: https://doi.org/10.2478/itms-2013-0007
- Bodyanskiy, Ye., Pliss, I., Vynokurova, O. (2013). Flexible wavelet-neuro-fuzzy neuron in dynamic data mining tasks. Oil and Gas Power Engineering, 2 (20), 158–162. Available at: http://nbuv.gov.ua/UJRN/Nge_2013_2_18
- Haykin, S. (1998). Neural Networks: A Comprehensive Foundation. Prentice Hall, 842.
- Nelles, O. (2001). Nonlinear System Identification. Springer. doi: https://doi.org/10.1007/978-3-662-04323-3
- Wang, L.-X., Mendel, J. M. (1992). Fuzzy basis functions, universal approximation, and orthogonal least-squares learning. IEEE Transactions on Neural Networks, 3 (5), 807–814. doi: https://doi.org/10.1109/72.159070
- Kohonen, T. (1995). Self-Organizing Maps. Springer. doi: https://doi.org/10.1007/978-3-642-97610-0
- Kasabov, N. (2003). Evolving Connectionist Systems. Springer. doi: https://doi.org/10.1007/978-1-4471-3740-5
- Sugeno, M., Kang, G. T. (1988). Structure identification of fuzzy model. Fuzzy Sets and Systems, 28 (1), 15–33. doi: https://doi.org/10.1016/0165-0114(88)90113-3
- Ljung, L. (1999). System Identification. Theory for the User. PTR Prentice Hall, Upper Saddle River, 609. Available at: https://www.twirpx.com/file/277211/
- Otto, P., Bodyanskiy, Y., Kolodyazhniy, V. (2003). A new learning algorithm for a forecasting neuro-fuzzy network. Integrated Computer-Aided Engineering, 10 (4), 399–409. doi: https://doi.org/10.3233/ica-2003-10409
- Narendra, K. S., Parthasarathy, K. (1990). Identification and control of dynamical systems using neural networks. IEEE Transactions on Neural Networks, 1 (1), 4–27. doi: https://doi.org/10.1109/72.80202
- Petruk, S., Zhyvotovskyi, R., Shyshatskyi, A. (2018). Mathematical Model of MIMO. 2018 International Scientific-Practical Conference Problems of Infocommunications. Science and Technology (PIC S&T), 7–11. doi: https://doi.org/10.1109/infocommst.2018.8632163
- Zhyvotovskyi, R., Shyshatskyi, A., Petruk, S. (2017). Structural-semantic model of communication channel. 2017 4th International Scientific-Practical Conference Problems of Infocommunications. Science and Technology (PIC S&T), 524–529. doi: https://doi.org/10.1109/infocommst.2017.8246454
- Alieinykov, I., Thamer, K. A., Zhuravskyi, Y., Sova, O., Smirnova, N., Zhyvotovskyi, R. et. al. (2019). Development of a method of fuzzy evaluation of information and analytical support of strategic management. Eastern-European Journal of Enterprise Technologies, 6 (2 (102)), 16–27. doi: https://doi.org/10.15587/1729-4061.2019.184394
- Koshlan, A., Salnikova, O., Chekhovska, M., Zhyvotovskyi, R., Prokopenko, Y., Hurskyi, T. et. al. (2019). Development of an algorithm for complex processing of geospatial data in the special-purpose geoinformation system in conditions of diversity and uncertainty of data. Eastern-European Journal of Enterprise Technologies, 5 (9 (101)), 35–45. doi: https://doi.org/10.15587/1729-4061.2019.180197
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Copyright (c) 2020 Volodymyr Dudnyk, Yuriy Sinenko, Mykhailo Matsyk, Yevhen Demchenko, Ruslan Zhyvotovskyi, Iurii Repilo, Oleg Zabolotnyi, Alexander Simonenko, Pavlo Pozdniakov, Andrii Shyshatskyi
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