Development of estimation and forecasting method in intelligent decision support systems

Authors

DOI:

https://doi.org/10.15587/1729-4061.2021.232718

Keywords:

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

Abstract

The method of estimation and forecasting in intelligent decision support systems was developed. The essence of the method is the analysis of the current state of the object and short-term forecasting of the object state. Objective and complete analysis is achieved by using improved fuzzy temporal models of the object state and an improved procedure for processing the original data under uncertainty. Also, the possibility of objective and complete analysis is achieved through an improved procedure for forecasting the object state and an improved procedure for learning evolving artificial neural networks. The concepts of fuzzy cognitive model are related by subsets of influence fuzzy degrees, arranged in chronological order, taking into account the time lags of the corresponding components of the multidimensional time series. The method is based on fuzzy temporal models and evolving artificial neural networks. The peculiarity of the method is the possibility of taking into account the type of a priori uncertainty about the object state (full awareness of the object state, partial awareness of the object state and complete uncertainty about the object state). The possibility to clarify information about the object state is achieved using an advanced training procedure. It consists in training the synaptic weights of the artificial neural network, the type and parameters of the membership function, as well as the architecture of individual elements and the architecture of the artificial neural network as a whole. The object state forecasting procedure allows conducting multidimensional analysis, consideration, and indirect influence of all components of a multidimensional time series with their different time shifts relative to each other under uncertainty. The method provides an increase in data processing efficiency at the level of 15–25% using additional advanced procedures.

Author Biographies

Qasim Abbood Mahdi, Al Taff University College

PhD, Head of  Department

Computer Technologies Engineering Department

Andrii Shyshatskyi, Central Scientifically-Research Institute of Armaments and Military Equipment of the Armed Forces of Ukraine

PhD, Senior Researcher

Research Department of Electronic Warfare Development

Yevgen Prokopenko, Ivan Chernyakhovsky National Defense University of Ukraine

PhD, Chief

Research laboratory of Problems of Development of Strategic Communications

Educational and Scientific Center for Strategic Communications in the Field of National Security and Defense

Tetiana Ivakhnenko, Central Scientifically-research institute of Arming and Military Equipment of the Armed Forces of Ukraine

PhD, Leading Researcher

Research Department of the Development of Anti-aircraft Missile Systems and Complexes

Dmytro Kupriyenko, National Academy of the State Border Guard Service of Ukraine named after Bohdan Khmelnytskyi

Doctor of Military Sciences, Professor

Vira Golian, Kharkiv National University of Radio Electronics

PhD, Assosiate Profesor

Department of Software Engineering

Roman Lazuta, Military Institute of Telecommunications and Information Technologies named after Heroes of Kruty

Senior Researcher

Scientific Center

Serhii Kravchenko, Poltava State Agrarian University

PhD, Associate Professor

Department of Information Systems and Technologies

Nadiia Protas, Poltava State Agrarian University

PhD, Associate Professor

Department of Information Systems and Technologies

Alexander Momit, Central Scientifically-research Institute of Arming and Military Equipment of the Armed Forces of Ukraine

Senior Researcher

Research Department of  Development of Anti-aircraft Missile Systems and Complexes

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Published

2021-06-30

How to Cite

Mahdi, Q. A., Shyshatskyi, A., Prokopenko, Y., Ivakhnenko, T. ., Kupriyenko, D. ., Golian, V., Lazuta, R., Kravchenko, S. ., Protas, N., & Momit, A. (2021). Development of estimation and forecasting method in intelligent decision support systems. Eastern-European Journal of Enterprise Technologies, 3(9(111), 51–62. https://doi.org/10.15587/1729-4061.2021.232718

Issue

Vol. 3 No. 9(111) (2021): Information and controlling system

Section

Information and controlling system

License

Copyright (c) 2021 Qasim Abbood Mahdi, Andrii Shyshatskyi, Yevgen Prokopenko Prokopenko, Tetiana Ivakhnenko, Dmytro Kupriyenko, Vira Golian, Roman Lazuta, Serhii Kravchenko, Nadiia Protas, Alexander Momit

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