Improvement of the method of estimation and forecasting of the state of the monitoring object in intelligent decision support systems
| Autor: | Oleksandr Shcheptsov, Areej Adnan Abed, Andrii Shyshatskyi, Serhii Kravchenko, Mykola Dieniezhkin, Nadiia Protas, Iurii Repilo, Ruslan Zhyvotovskyi, Spartak Hohoniants, Iryna Zhyvylo |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Decision support system
Automatic control Computer science Energy Engineering and Power Technology computer.software_genre Fuzzy logic Industrial and Manufacturing Engineering state forecasting Management of Technology and Innovation T1-995 Industry Electrical and Electronic Engineering Technology (General) Multidimensional analysis Data processing Artificial neural network Applied Mathematics Mechanical Engineering Object (computer science) HD2321-4730.9 Computer Science Applications Control and Systems Engineering training of artificial neural networks Data mining computer decision support systems artificial neural networks Membership function |
| Zdroj: | Eastern-European Journal of Enterprise Technologies, Vol 4, Iss 3(112), Pp 43-55 (2021) |
| ISSN: | 1729-4061 1729-3774 |
| Popis: | In order to objectively and completely analyze the state of the monitored object with the required level of efficiency, the method for estimating and forecasting the state of the monitored object in intelligent decision support systems was improved. The essence of the method is to provide an analysis of the current state of the monitored object and short-term forecasting of the state of the monitored object. Objective and complete analysis is achieved using advanced fuzzy temporal models of the object state, taking into account the type of uncertainty and noise of initial data. The novelty of the method is the use of an improved procedure for processing initial data in conditions of uncertainty, an improved procedure for training artificial neural networks and an improved procedure for topological analysis of the structure of fuzzy cognitive models. The essence of the training procedure is the training of synaptic weights of the artificial neural network, the type and parameters of the membership function and the architecture of individual elements and the architecture of the artificial neural network as a whole. The procedure of forecasting the state of the monitored object allows for multidimensional analysis, accounting and indirect influence of all components of the multidimensional time series with their different time shifts relative to each other in conditions of uncertainty. The method allows increasing the efficiency of data processing at the level of 12–18% using additional advanced procedures. The proposed method can be used in decision support systems of automated control systems (ACS DSS) for artillery units, special-purpose geographic information systems. It can also be used in ACS DSS for aviation and air defense and ACS DSS for logistics of the Armed Forces of Ukraine |
| Databáze: | OpenAIRE |
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