Oleg Sova
@viti.edu.ua
Information Technology Faculty, ACS Department
Military Institute of Telecommunications and Information Technologies named after Kruty Heroes
RESEARCH INTERESTS
Cloud technologies, artificial intelligence, management systems, data processing
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Khudhair Abed Thamer, Oleg Sova, Olena Shaposhnikova, Volodymyr Yashchenok, Iraida Stanovska, Serhii Shostak, Oleksandr Rudenko, Serhii Petruk, Olha Matsyi, and Svitlana Kashkevich
Private Company Technology Center
The object of the study is decision support systems. The subject of the study is the decision-making process in management problems using a combined bio-inspired algorithm, consisting of: – the improved wolf optimization algorithm and the improved sparrow search algorithm – for solving optimization problems regarding the object state; – an advanced genetic algorithm – for selecting the best agents in flocks; – an advanced training method – for deep training of agents to improve the optimization characteristics of agents. A solution search method using an improved bio-inspired algorithm is proposed. The method has the following sequence of actions: – input of initial data; – initialization of the search for a flock of sparrows and its parameters; – ranking and selection of sparrow agents using an advanced genetic algorithm; – updating the sparrow location for the discoverer; – checking the conditions for updating the position of sparrows; – initialization of additional search parameters; − running the gray wolf optimization algorithm; – training agents’ knowledge bases; – determining the amount of necessary computing resources of the intelligent decision support system. The originality of the proposed method lies in the combined use of bio-inspired algorithms, setting agents taking into account the uncertainty of the initial data, advanced global and local search procedures. The method makes it possible to increase the efficiency of data processing at the level of 19 % using additional improved procedures. The proposed method should be used to solve the problems of evaluating complex and dynamic processes in the interest of solving national security problems
Ghadeer Al Mamoori, Oleg Sova, Oleksandr Zhuk, Iurii Repilo, Borys Melnyk, Sviatoslav Sus, Mariia Bondarchuk, Svitlana Kashkevich, Mykola Moroz, and Oksana Klyuchak
Private Company Technology Center
The object of research is the decision making support systems. The subject of the research is the decision making process in management tasks using an advanced jumping frog algorithm (JFA), an advanced genetic algorithm and evolving artificial neural networks. A method of finding solutions with the use of improved JFA is proposed. The research is based on the JFA to find a solution regarding the object state. Evolving artificial neural networks are used to train frog agents (FA). The method has the following sequence of actions: – an input of initial data; – processing of initial data taking into account the degree of uncertainty; – calculation of the value of the criterion of optimality of each permutation from the initial FA population – global search of FA; – an improvement of the FA position in the search space; – a regulation of the speed of vehicle movement. – an improvement of the working conditions of JFA; – the FA rearrangement; – an unification of all memplexes into one group; – the verification of the fulfillment of the conditions of JFA operation; – the search for the best FA; – training of the FA knowledge bases. The originality of the proposed method consists in the arrangement of the FA taking into account the uncertainty of the initial data, the improved procedures of global and local edge taking into account the degree of data noise about the analysis object state, the adjustment of the degree of data noise during the FA movement, the adjustment of the speed of the FA movement. Also, the peculiarity of the proposed method is the use of an improved procedure for FA training. The use of the method makes it possible to increase the efficiency of data processing at the level of 14–18 % due to the use of additional improved procedures. The proposed method should be used to solve the problems of evaluating complex and dynamic processes in the interests of solving national security problems
Olena Nechyporuk, Oleg Sova, Andrii Shyshatskyi, Serhii Kravchenko, Oleksii Nalapko, Oleh Shknai, Serhii Klimovych, Olha Kravchenko, Oleksandr Kovbasiuk, and Anton Bychkov
Private Company Technology Center
A method of complex analysis and multidimensional forecasting of the state of intelligence objects is proposed to increase the accuracy of their state assessment. The object of research is decision support systems. The subject of research is the process of decision-making in management problems using artificial intelligence methods. The hypothesis of research is to increase the efficiency of decision-making with a given assessment reliability. The proposed method is based on a combination of fuzzy cognitive and temporal models, an advanced cat swarm optimization algorithm and evolving artificial neural networks. The method has the following sequence of actions: ‒ input of initial data; ‒ processing of initial data taking into account uncertainty about the state of heterogeneous intelligence objects; ‒ construction of a fuzzy temporal ontological model of heterogeneous intelligence objects; ‒ conclusion on the state of heterogeneous intelligence objects; ‒ correction of the fuzzy temporal ontological model; ‒ building a fuzzy relational temporal cognitive model of heterogeneous intelligence objects and forecasting the state of the intelligence object; ‒ training knowledge bases on heterogeneous intelligence objects. The training procedure consists in learning 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 method makes it possible to increase the efficiency of data processing at the level of 18–25 % by using additional improved procedures. The proposed method should be used to solve the problems of evaluating complex and dynamic heterogeneous intelligence objects, characterized by a high degree of complexity.
Oleg Sova, Yurii Zhuravskyi, Yuliia Vakulenko, Andrii Shyshatskyi, Olha Salnikova, and Oleksii Nalapko
OU Scientific Route
Decision making support systems are actively used in the processing of large data sets, process forecasting, providing information support to the decision-making process by decision-makers. However, there are problems with the transmission of information: the transmission of information takes place in a complex electronic environment against the background of interference; radio communication systems are the objects of primary fire damage due to high radio visibility. This article develops the methodological principles of routing in special communication networks in the conditions of fire damage and electronic suppression. The purpose of this research is to increase the efficiency of information transfer under the influence of destabilizing factors. The proposed methodological principles are based on the theory of artificial intelligence. The research presents a mathematical formulation of the problem of routing in special-purpose radio networks and developed a method of routing in special-purpose radio networks.
 The efficiency of information processing is achieved through training in the architecture of artificial neural networks; taking into account the type of uncertainty of the information to be assessed; use of the ant algorithm. The approbation of the use of the offered technique on the example of the estimation of information transfer in the conditions of influence of destabilizing factors is carried out. The proposed methodological principles should be used in the development of software for programmable devices of communication and in the modernization of existing and development of new radio communication devices. This example showed an increase in the efficiency of information transmission in radio communication systems at the level of 15–25 % on the criterion of efficiency
Mykhailo Koval, Oleg Sova, Andrii Shyshatskyi, Yurii Artabaiev, Nataliia Garashchuk, Yurii Yivzhenko, Yuriy Luscshay, Liudmyla Dovhopoliuk, Oles Haidenko, and Mykola Dorofeev
Private Company Technology Center
The problem that is solved in the research is to increase the efficiency of assessing the state of the monitoring object while ensuring the given reliability, regardless of the hierarchy of the monitoring object. The object of research is decision support systems. The subject of the research is the process of evaluating the monitoring object using bio-inspired algorithms. The hypothesis of the research is the need to increase the efficiency of the assessment of the state of the monitoring object with the given reliability. In the course of the research, an improved method of increasing the efficiency of decision-making based on bio-inspired algorithms was proposed. General provisions of artificial intelligence theory were used to solve the problem of analyzing the object state in intelligent decision support systems. The essence of improvement is to use the following procedures: − taking into account the type of uncertainty about the state of the monitoring object (full uncertainty, partial uncertainty and full awareness); − taking into account the degree of noise in the data on the state of the monitoring object. Noise refers to the degree of information distortion created by the enemy’s means of electronic and cyber warfare; − using the ant colony optimization algorithm and the genetic algorithm to find the path metric while assessing the state of the monitoring object; − deep learning of synthesized ants using evolving artificial neural networks. An example of using the proposed method in assessing the state of the operational situation of a group of troops (forces) is presented. The specified example showed a 15−22 % increase in the efficiency of data processing using additional improved procedures
Mykhailo Koval, Oleg Sova, Oleksandr Orlov, Andrii Shyshatskyi, Yurii Artabaiev, Oleh Shknai, Andrii Veretnov, Oleksandr Koshlan, Yevhen Zhyvylo, and Iryna Zhyvylo
Private Company Technology Center
The object of the research is a special-purpose communication system. The relevance of the research lies in the need for complex management of resources of special-purpose communication systems. The resources of the special-purpose communication system are defined as: spatial, temporal, frequency and hardware resources. Destabilizing factors include: intentional interference; denial-of-service cyber attacks and fire damage to individual elements of the special-purpose communication system. The method of complex management of resources of special-purpose communication systems was improved. The difference between the proposed method and the known ones is that the specified method contains improved procedures: ‒ determination of the impact of destabilizing factors on the special-purpose communication system; ‒ description of special-purpose communication systems of various architectures; ‒ determination of the rational route of information transmission and operation mode of communication devices in the general special-purpose communication system; ‒ consideration of uncertainty about the state of the special-purpose communication system; ‒ determination of the number of necessary forces and means of communication, which must be increased for the full functioning of the special communication system. The improved method provides a gain of 20‒26 % compared to classical approaches to the management of resources of special-purpose communication systems. The improved method can be used at the control points of the communication system of groups of troops (forces) while planning the organization of communication and at the stage of operational management of the communication system.
Oleg Sova, Andrii Shyshatskyi, Viktor Ostapchuk, Yurii Zhuravskyi, Maksym Rohovets, Ihor Borysov, Viktor Bovsunovskyi, Yuriy Artabaev, Oleksandr Trotsko, and Ihor Pylypchuk
Private Company Technology Center
The object of research is multi-antenna systems with spectrally efficient special purpose signals. The problematic issue, the solution of which is devoted to this research, is the improvement of immunity to interference of multi-antenna systems with spectrally efficient special purpose signals. A technique for improving the immunity of multi-antenna systems with spectrally efficient special-purpose signals under the influence of destabilizing factors has been developed. A distinctive feature of the proposed methodology is the use of an improved pre-coding procedure, evaluation of the channel state of multi-antenna radio communication systems with spectrally efficient signals by several indicators. The improved channel state estimation procedure consists in estimating channel bit error probability, channel state frequency response, and channel state impulse response. The formation of an estimate of the channel state for each of the assessment indicators takes place on a separate layer of the neural network using the apparatus of fuzzy sets, after which a generalized estimate is formed at the output of the neural network. The novelty of the proposed method also consists in the use of an improved procedure for forecasting the channel state of multi-antenna systems with spectrally efficient signals. The essence of the proposed procedure is the use of fuzzy cognitive models and an artificial neural network to predict the state of the channels of multi-antenna systems with spectrally efficient signals. Based on the results of the research, it was established that the proposed method allows to increase the immunity of multi-antenna systems with spectrally efficient signals according to the 8×8 scheme and 64 subcarriers by 20–25 % compared to the known ones.
Oleg Sova, Hryhorii Radzivilov, Andrii Shyshatskyi, Dmytro Shevchenko, Bohdan Molodetskyi, Vitalii Stryhun, Yurii Yivzhenko, Yevhen Stepanenko, Nadiia Protas, and Oleksii Nalapko
Private Company Technology Center
The features of modern military conflicts require significantly increasing requirements for the efficiency of determining a rational route for the transmission of information. It is necessary to develop algorithms (methods and techniques) that are able for a limited time and with a high degree of reliability to determine the rational route of information transmission in complex hierarchical information transmission systems. The following tasks were solved in the research: the task of information transfer in special purpose networks was set; the algorithm of realization of a method of efficiency increase of information transfer is defined; simulation of the process of information transfer in the communication networks of a group of troops (forces) was carried out. The essence of the proposed method is to use the ant algorithm and their further training. The method has the following sequence of actions: input of initial data; determining the degree of uncertainty and noise of the original data, determining the set of acceptable solutions, determining belonging to a certain class. The next step is to determine the route of information transfer, taking into account the impact of destabilizing factors, taking into account computing power and training ants. The novelty of the method is to take into account the type of uncertainty and noise in the data and take into account the available computing resources of the communication network. The novelty of the method also lies in the use of advanced training procedures using the apparatus of evolving artificial neural networks and selective use of system resources by connecting only the required number of agents (ants). The method allows to build a rational route of information transfer taking into account the influence of destabilizing factors. The use of the method allows to achieve an increase in the efficiency of information transfer at the level of 11-16% through the use of additional advanced procedures
Oleg Sova, Hryhorii Radzivilov, Andrii Shyshatskyi, Pavel Shvets, Valentyna Tkachenko, Serhii Nevhad, Oleksandr Zhuk, Serhii Kravchenko, Bohdan Molodetskyi, and Hennadii Miahkykh
Private Company Technology Center
The peculiarities of modern military conflicts significantly increase the requirements for the efficiency of object state assessment. Therefore, it is necessary to develop algorithms (methods and techniques) that can assess the state of the monitoring object from different sources of intelligence for a limited time and with a high degree of reliability. Accurate and objective object analysis requires multi-parameter estimation with significant computational costs. That is why the following tasks were solved in the study: the formalization of the assessment of monitoring objects was carried out, a method of increasing the efficiency of assessing the condition of monitoring objects was developed and an efficiency assessment was carried out. The essence of the proposed method is the hierarchical hybridization of binary classifiers and their subsequent training. The method has the following sequence of actions: determining the degree of uncertainty, constructing a classifier tree, determining belonging to a particular class, determining object parameters, pre-processing data about the object of analysis and hierarchical traversal of the tree. The novelty of the method lies in taking into account the type of uncertainty and noise of the data and taking into account the available computing resources of the object state analysis system. The novelty of the method also lies in the use of combined training procedures (lazy training and training procedure for evolving neural networks) and selective use of system resources by connecting only the necessary types of detectors. The method allows you to build a top-level classifier using various low-level schemes for combining them and aggregating compositions. The method increases the efficiency of data processing by 12–20 % using additional advanced procedures
Taras Hurskyi, Oleg Sova, and Serhiy Boholiy
IEEE
One of the promising areas for improving the efficiency of military VHF radio networks is the use of repeaters on board UAVs. Air repeater, compared to ground one, allows to significantly increase the coverage area and connectivity of the radio network, to reduce transmitter power by providing direct radio-visibility between the repeater and almost all radios in the network. From the point of view of the jammer there are four possible ways of electronic suppressing of the radio network, using air repeater (AR), which include suppressing the receiver of the AR or terrestrial radios by terrestrial jammer or by the air jammer. The calculations of the expected levels of the useful frequency hopping signal and the noise barrier interference at the entrances of the receivers of the terrestrial radio and the airborne (aboard the UAV) repeater during jamming from terrestrial and airborne (from UAV) means of electronic warfare have been performed. The directions of increase of anti-jammingness of radio networks, using air repeaters, are determined.
Yurii Zhuravskyi, Oleg Sova, Serhii Korobchenko, Vitaliy Baginsky, Yurii Tsimura, Leonid Kolodiichuk, Pavlo Khomenko, Nataliia Garashchuk, Olena Orobinska, and Andrii Shyshatskyi
Private Company Technology Center
Accurate and objective object analysis requires multi-parameter estimation with significant computational costs. A methodological approach to improve the accuracy of assessing the state of the monitored object is proposed. This methodological approach is based on a combination of fuzzy cognitive models, advanced genetic algorithm and evolving artificial neural networks. The methodological approach has the following sequence of actions: building a fuzzy cognitive model; correcting the fuzzy cognitive model and training knowledge bases. The distinctive features of the methodological approach are that the type of data uncertainty and noise is taken into account while constructing the state of the monitored object using fuzzy cognitive models. The novelties while correcting fuzzy cognitive models using a genetic algorithm are taking into account the type of data uncertainty, taking into account the adaptability of individuals to iteration, duration of the existence of individuals and topology of the fuzzy cognitive model. The advanced genetic algorithm increases the efficiency of correcting factors and the relationships between them in the fuzzy cognitive model. This is achieved by finding solutions in different directions by several individuals in the population. The training procedure consists in learning the 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 use of the method allows increasing the efficiency of data processing at the level of 16–24 % using additional advanced procedures. The proposed methodological approach should be used to solve the problems of assessing complex and dynamic processes characterized by a high degree of complexity.
Oleg Sova, Andrii Shyshatskyi, Olha Salnikova, Oleksandr Zhuk, Oleksandr Trotsko, and Yaroslav Hrokholskyi
OU Scientific Route
Decision making support systems (DSS) are actively used in all spheres of human life. The system of the electronic environment analysis is not an exception. However, there are a number of problems in the analysis of the electronic environment, for example: the signals are analyzed in a complex electronic environment against the background of intentional and natural interference. Input signals do not match the standards, and their interpretation depends on the experience of the operator (expert), the completeness of additional information on a particular task (uncertainty condition). The best solution in this situation is found in the integration with the data of the information system analysis of the electronic environment, artificial neural networks and fuzzy cognitive models. Their advantages are also the ability to work in real time and quick adaptation to specific situations. The article develops a method for assessing and forecasting the electronic environment.
 Improving the efficiency of evaluation information processing is achieved through the use of evolving neuro-fuzzy artificial neural networks; learning not only the synaptic weights of the artificial neural network, the type and parameters of the membership function. The efficiency of information processing is also achieved through training in the architecture of artificial neural networks; taking into account the type of uncertainty of the information that has to be assessed; synthesis of rational structure of fuzzy cognitive model. It reduces the computational complexity of decision-making; has no accumulation of learning error of artificial neural networks as a result of processing the information coming to the input of artificial neural networks. The example of assessing the state of the electronic environment showed an increase in the efficiency of assessment at the level of 15–25 % on the efficiency of information processing
Oleg Sova, Andrii Shyshatskyi, Dmytro Malitskyi, Oleksandr Zhuk, Oleksandr Gaman, Valerii Hordiichuk, Vitalii Fedoriienko, Andrii Kokoiko, Vitalii Shevchuk, and Mykhailo Sova
Private Company Technology Center
Olha Salnikova, Olga Cherviakova, Oleg Sova, Ruslan Zhyvotovskyi, Serhii Petruk, Taras Hurskyi, Andrii Shyshatskyi, Andrey Nos, Yevhenii Neroznak, and Ihor Proshchyn
Private Company Technology Center
Nowadays, artificial intelligence has entered into all spheres of human activity. However, there are some problems in the analysis of objects, for example, there is a priori uncertainty about the state of objects and the analysis takes place in a difficult situation against the background of intentional (natural) interference and uncertainty. The best solution in this situation is to integrate with the data analysis of information systems and artificial neural networks. This paper develops an improved method for finding solutions for neuro-fuzzy expert systems. The proposed method allows increasing the efficiency and reliability of making decisions about the state of the object. Increased efficiency is achieved through the use of evolving neuro-fuzzy artificial neural networks, as well as an improved procedure for their training. Training of evolving neuro-fuzzy artificial neural networks is due to learning their architecture, synaptic weights, type and parameters of the membership function, as well as the application of the procedure of reducing the dimensionality of the feature space. The analysis of objects also takes into account the degree of uncertainty about their condition. In the proposed method, when searching for a solution, the same conditions are calculated once, which speeds up the rule revision cycle and instead of the same conditions of the rules, references to them are used. This reduces the computational complexity of decision-making and does not accumulate errors in the training of artificial neural networks as a result of processing the information coming to the input of artificial neural networks. The use of the proposed method was tested on the example of assessing the state of the radio-electronic environment. This example showed an increase in the efficiency of assessment at the level of 20–25 % by the efficiency of information processing
Hennadii Pievtsov, Oleksandr Turinskyi, Ruslan Zhyvotovskyi, Oleg Sova, Oleksii Zvieriev, Boris Lanetskii, and Andrii Shyshatskyi
OU Scientific Route
Nowadays, artificial intelligence has entered into all spheres of our life. The system of analysis of the electronic environment is not an exception. However, there are a number of problems in the analysis of the electronic environment, namely the signals. They are analyzed in a complex electronic environment against the background of intentional and natural interference. Also, the input signals do not match the standards due to the influence of different types of interference. Interpretation of signals depends on the experience of the operator, the completeness of additional information on a specific condition of uncertainty. The best solution in this situation is to integrate with the data of the information system analysis of the electronic environment and artificial neural networks. Their advantage is also the ability to work in real time and quick adaptation to specific situations. These circumstances cause uncertainty in the conditions of the task of signal recognition and fuzzy statements in their interpretation, when the additional involved information may be incomplete and the operator makes decisions based on their experience. That is why, in this article, an improved method for finding solutions for neuro-fuzzy expert systems of analysis of the electronic environment is developed. Improving the efficiency of information processing (reducing the error) of evaluation is achieved through the use of neuro-fuzzy artificial neural networks that are evolving and learning not only the synaptic weights of the artificial neural network, but also the type and parameters of the membership function. High efficiency of information processing is also achieved through training in the architecture of artificial neural networks by taking into account the type of uncertainty of the information that has to be assessed and work with clear and fuzzy products. This reduces the computational complexity of decision-making and absence of accumulation of an error of training of artificial neural networks as a result of processing of the arriving information on an input of artificial neural networks. The use of the proposed method was tested on the example of assessing the state of the electronic environment. This example showed an increase in the efficiency of assessment at the level of 20–25 % on the efficiency of the processing information
Oleg Sova, Andrii Shyshatskyi, Yurii Zhuravskyi, Olha Salnikova, Oleksandr Zubov, Ruslan Zhyvotovskyi, Іgor Romanenko, Yevhen Kalashnikov, Artem Shulhin, and Alexander Simonenko
Private Company Technology Center
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 networks
Oleg Sova, Oleksandr Turinskyi, Andrii Shyshatskyi, Volodymyr Dudnyk, Ruslan Zhyvotovskyi, Yevgen Prokopenko, Taras Hurskyi, Valerii Hordiichuk, Anton Nikitenko, and Artem Remez
Private Company Technology Center
The algorithm to train artificial neural networks for intelligent decision support systems has been constructed. A distinctive feature of the proposed algorithm is that it conducts training not only for synaptic weights of an artificial neural network, but also for the type and parameters of membership function. In case of inability to ensure the assigned quality of functioning of artificial neural networks due to training of parameters of artificial neural network, the architecture of artificial neural networks is trained. The choice of the architecture, type and parameters of membership function occurs taking into consideration the computation resources of the facility and taking into consideration the type and the amount of information entering the input of an artificial neural network. In addition, when using the proposed algorithm, there is no accumulation of an error of artificial neural networks training as a result of processing the information entering the input of artificial neural networks.Development of the proposed algorithm was predetermined by the need to train artificial neural networks for intelligent decision support systems in order to process more information given the unambiguity of decisions being made. The research results revealed that the specified training algorithm provides on average 16–23 % higher the efficiency of training artificial neural networks training that is on average by 16–23 % higher and does not accumulate errors in the course of training. The specified algorithm will make it possible to conduct training of artificial neural networks; to determine effective measures to enhance the efficiency of functioning of artificial neural networks. The developed algorithm will also enable the improvement of the efficiency of functioning of artificial neural networks due to training the parameters and the architecture of artificial neural networks. The proposed algorithm reduces the use of computational resources of decision support systems. The application of the developed algorithm makes it possible to work out the measures aimed at improving the effectiveness of training artificial neural networks and to increase the efficiency of information processing
Oleg Sova, Victor Golub, Andrii Shyshatskyi, Viktor Ostapchuk, Oleksii Nalapko, and Halyna Zubrytska
IEEE
The method of forecasting the time of overloading of data transfer routes in the mobile radio link (MRL), which is constructed using the neural network of Elman based on the calculation of the potential of neurons in the network is presented. The essence of the method is to make a decision on finding new routes based on the predicted time of overloading the data transfer routes in the MRL. This method allows predicting the time of overloading of data transfer routes in the MRL due to the reduction of the computational complexity of the neural network and the application of the algorithm for training the neural network.
Ihor Alieinykov, Khudhair Abed Thamer, Yurii Zhuravskyi, Oleg Sova, Nataliia Smirnova, Ruslan Zhyvotovskyi, Serhiy Hatsenko, Sergii Petruk, Rostislav Pikul, and Andrii Shyshatskyi
Private Company Technology Center
The method of fuzzy evaluation of information and analytical support of strategic management is developed. A distinctive feature of the proposed method a flexible hierarchical structure of indicators. This allows reducing the task of multicriteria evaluation of alternatives to a single criterion or using the vector of indicators for selection and it provides an opportunity of fuzzy presentation of indicators and compatibility relations between them, which can realize the different nature of relationships. Also, this method allows implementing forward and backward fuzzy evaluation and takes into account the different significance of individual indicators by using the weight of the indicator. The development of the proposed method is due to the need for processing more information and moderate computational complexity.The research found that the proposed method has a computational complexity 10–15 % less than the methods used to evaluate the effectiveness of strategic management decisions. This method will allow evaluating the state of information and analytical support and identifying effective measures to improve the effectiveness of information and analytical support of strategic management. The method allows increasing the speed of evaluating the state of information and analytical support, reducing the use of computing resources of support and decision-making systems, developing measures aimed at improving the effectiveness of information and analytical support. It is advisable to use this method in decision support systems to evaluate strategic management issues
O. I. Lysenko, A. V. Romanyuk, V. A. Romanyuk, and Y. O. Sova
IEEE
Intelligent control system hierarchical model of sensor and MANET networks is proposed in the paper. Proposed model is based in the conceptual representation of the intelligent control systems as a hierarchical structure with vertical connections that define management tasks subordination in the MANET and sensor networks.
Valeriy Romanyuk, Oleg Sova, Anton Romanyuk, and Sergey Salnyk
IEEE
The hierarchical model of interaction between mobile nodes in the MANET using fuzzy logic is proposed in the paper. Proposed model is based in the conceptual representation of the mobile radio network control systems as a hierarchical structure with vertical connections that define management tasks subordination using the hierarchical control system and fuzzy logic.
V.A. Romanyuk and O.Ya. Sova
IEEE
The hierarchical model of interaction between mobile sensors in the wireless sensor networks using unmanned aerial vehicles is proposed in the paper. Proposed model is based in the conceptual representation of the wireless sensor networks control systems as a hierarchical structure with vertical connections that define management tasks subordination in the wireless sensor networks using unmanned aerial vehicles.