Development of an algorithm for complex processing of geospatial data in the special-purpose geoinformation system in conditions of diversity and uncertainty of data

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processing of data coming from various technical devices of information extraction [1, 2]. The main advantage of information retrieval technologies over other information technologies is the ability to combine different types of information from different da-1. Introduction The rapid development of the number of information sources in the world significantly complicates the process of data extraction and necessitates their integration and

Section: Development Of An Algorithm For Indexing Heterogeneous Datamentioning

confidence: 94%

Development of complex methodology of processing heterogeneous data in intelligent decision support systems

Zuiev¹,

Zhyvotovskyi²,

Zvieriev³

et al. 2020

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“…In addition, most users today need not only routes with the fewest hops to the destination, but also routes that can meet other important needs. Such users often need to provide support for QoS [19,20] and to be able to take into account the energy component [13,15,[21][22][23][24] due to the fact that in special-purpose networks there may be a significant number of nodes running on batteries, taking into account the speed of the communication channel, the delay time between the sender of the packet and its recipient, the number of available routes and the reliability of packet delivery by the appropriate route.…”

Section: Development Of An Algorithm For Adaptive Control Of Militarymentioning

confidence: 99%

“…According to [13][14][15][16]21], the importance of REE indicators can be considered as a non-metric criterion of usefulness (NCU).…”

mentioning

confidence: 99%

Development of a method of adaptive control of military radio network parameters

Nalapko

Shyshatskyi

Ostapchuk

et al. 2021

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A method of adaptive control of military radio network parameters has been developed. This method allows predicting suppressed frequencies by electronic warfare devices, determining the topology of the military radio network. Also, this method allows determining rational routes of information transmission and operating mode of radio communications. Forecasting of the electronic environment is characterized by recirculation of input data for one count, resampling on a logarithmic time scale, finding a forecast for the maximum value of entropy and resampling the forecast on the exponential time scale. The developed method allows choosing a rational network topology. The choice of topology of the military radio communication system is based on the method of ant multi-colony system. The main idea of the new option of ant colony optimization is that instead of one colony of the traditional ant algorithm several colonies are used that work together in a common search space. However, this procedure additionally takes into account the type of a priori uncertainty and the evaporation coefficient of the pheromone level. The proposed method allows choosing a rational route for information transmission. The proposed procedure is based on an improved DSR algorithm. This method uses several operating modes of radio communications, namely the technology of multi-antenna systems with noise-like signals, with pseudo-random adjustment of the operating frequency and with orthogonal frequency multiplexing. The developed method provides a gain of 10‒16 % compared to conventional management approaches

“…At this stage, the KB is formed on the basis of the expressions (4)- (17). While converting values about the object and the environment into fuzzy rules, the value of uncertainty about the radiation sources is taken into account, according to the expressions (10)-(12) [23].…”

Section: Development Of Basic Procedures and Algorithm For Implementimentioning

confidence: 99%

Development of an improved method for finding a solution for neuro-fuzzy expert systems

Salnikova¹,

Cherviakova²,

Sova³

et al. 2020

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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