Development of a methodology for training artificial neural networks for intelligent decision support systems
| Autor: | Oleg Sova, Andrii Shyshatskyi, Yurii Zhuravskyi, Olha Salnikova, Oleksandr Zubov, Ruslan Zhyvotovskyi, Іgor Romanenko, Yevhen Kalashnikov, Artem Shulhin, Alexander Simonenko |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Decision support system
Process (engineering) Computer science 020209 energy media_common.quotation_subject Computer Science::Neural and Evolutionary Computation 0211 other engineering and technologies Energy Engineering and Power Technology 02 engineering and technology information processing Industrial and Manufacturing Engineering Development (topology) Management of Technology and Innovation lcsh:Technology (General) 021105 building & construction 0202 electrical engineering electronic engineering information engineering lcsh:Industry Quality (business) Electrical and Electronic Engineering media_common training Quantitative Biology::Neurons and Cognition Artificial neural network business.industry Applied Mathematics Mechanical Engineering Information processing Training (meteorology) Computer Science Applications efficiency intelligent decision support systems Control and Systems Engineering lcsh:T1-995 lcsh:HD2321-4730.9 Artificial intelligence business artificial neural networks Membership function |
| Zdroj: | Eastern-European Journal of Enterprise Technologies, Vol 2, Iss 4 (104), Pp 6-14 (2020) |
| ISSN: | 1729-4061 1729-3774 |
| Popis: | The method of training artificial neural networks for intelligent decision support systems is developed. A distinctive feature of the proposed method is that it provides training not only of the synaptic weights of the artificial neural network, but also the type and parameters of the membership function. If it is impossible to provide the specified quality of functioning of artificial neural networks due to the learning of the parameters of the artificial neural network, the architecture of artificial neural networks is trained. The choice of architecture, type and parameters of the membership function is based on the computing resources of the tool and taking into account the type and amount of information supplied to the input of the artificial neural network. Due to the use of the proposed methodology, there is no accumulation of errors of training artificial neural networks as a result of processing information that is fed to the input of artificial neural networks. Also, a distinctive feature of the developed method is that the preliminary calculation data are not required for data calculation. The development of the proposed methodology is due to the need to train artificial neural networks for intelligent decision support systems in order to process more information with the uniqueness of decisions made. According to the results of the study, it is found that the mentioned training method provides on average 10–18% higher efficiency of training artificial neural networks and does not accumulate errors during training. This method will allow training artificial neural networks through the learning of parameters and architecture, identifying effective measures to improve the efficiency of artificial neural networks. This methodology will allow reducing the use of computing resources of decision support systems and developing measures aimed at improving the efficiency of training artificial neural networks; increasing the efficiency of information processing in artificial neural networks |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|