Improvement of the method of estimation and forecasting of the state of the monitoring object in intelligent decision support systems

Authors

DOI:

https://doi.org/10.15587/1729-4061.2021.237996

Keywords:

decision support systems, artificial neural networks, state forecasting, training of artificial neural networks

Abstract

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

Author Biographies

Areej Adnan Abed, Al-Maaref University College

Lecturer

Department of Logical Design

Iurii Repilo, National Defence University of Ukraine named after Ivan Cherniakhovskyi

Doctor of Military Sciences, Professor

Department of Missile Troops and Artillery

Ruslan Zhyvotovskyi, Central Scientific Research Institute of Armament and Military Equipment of the Armed Forces of Ukraine

PhD, Senior Researcher, Head of Research Department

Research Department of Development Armament and Military Equipment of Air Force

Andrii Shyshatskyi, Central Scientific Research Institute of Armament and Military Equipment of the Armed Forces of Ukraine

PhD, Senior Researcher

Research Department of Electronic Warfare Development

Spartak Hohoniants, National Defence University of Ukraine named after Ivan Cherniakhovskyi

PhD, Senior Researcher, Head of Research Department

Research Department of Prospects for the Development of Electronic Teaching Aids

Serhii Kravchenko, Poltava State Agrarian University

PhD, Associate Professor

Department of Information Systems and Technologies

Iryna Zhyvylo, National Scientific Center "M.D. Strazhesko Institute of Cardiology"

Candidate of Medical Sciences, Junior Researcher

Mykola Dieniezhkin, Central Scientific-Research Institute of Armed Forces of Ukraine

Doctor of Military Sciences, Senior Researcher, Leading Researcher

Scientific Research Department of Research Problems of Armed Forces Development

Nadiia Protas, Poltava State Agrarian University

PhD, Associate Professor

Department of Information Systems and Technologies

Oleksandr Shcheptsov, Naval Institute of the National University "Odessa Maritime Academy"

PhD, Head of Department

Department of Weapon

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Published

2021-08-31

How to Cite

Abed, A. A., Repilo, I., Zhyvotovskyi, R., Shyshatskyi, A., Hohoniants, S., Kravchenko, S., Zhyvylo, I., Dieniezhkin, M., Protas, N., & Shcheptsov, O. (2021). Improvement of the method of estimation and forecasting of the state of the monitoring object in intelligent decision support systems . Eastern-European Journal of Enterprise Technologies, 4(3(112), 43–55. https://doi.org/10.15587/1729-4061.2021.237996

Issue

Section

Control processes