Processing of signal information in problems of monitoring information security of unmanned autonomous objects

Викторович, Семенов Виктор; Сергеевич, Лебедев Илья

doi:10.17586/2226-1494-2019-19-3-492-498

Cited by 15 publications

(19 citation statements)

References 12 publications

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“…The Rete method was chosen as the basis for the development of an improved method for finding solutions for neuro-fuzzy expert systems for the analysis of the electronic environment [5,10,11]. The main disadvantage of the Rete method is the fact that it works only with clear products, which does not allow it to be used while processing the different types of data.…”

Section: The Development Of An Improved Methods For Finding Solutions mentioning

confidence: 99%

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Development of an Advanced Method of Finding Solutions for Neuro-Fuzzy Expert Systems of Analysis of the Radioelectronic Situation

Pievtsov

Turinskyi

Zhyvotovskyi

et al. 2020

EUREKA: Physics and Engineering

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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

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Section: The Development Of An Improved Methods For Finding Solutions mentioning

confidence: 99%

“…This model allows the use of not only linguistic variables but also classical variables. In this case, their values can also be compared with constants [5]. R is the set of relations between node vertices…”

Section: The Development Of An Improved Methods For Finding Solutions mentioning

confidence: 99%

Development of an Advanced Method of Finding Solutions for Neuro-Fuzzy Expert Systems of Analysis of the Radioelectronic Situation

Pievtsov

Turinskyi

Zhyvotovskyi

et al. 2020

EUREKA: Physics and Engineering

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“…In [10], a method for processing different types of acoustic information from different sources of origin to identify the level of information security of unmanned autonomous objects is developed. This method is based on the use of a two-layer neural network with sigmoid hidden neurons.…”

Section: The Aim and Objectives Of The Studymentioning

confidence: 99%

“…6 shows graphical estimates of the efficiency of data processing by the criterion of the number of computing operations of the method proposed in the paper. The comparison of the developed complex technique was carried out by comparing its efficiency with the known ones presented in [9,10,12]. Denote these works 1, 2 and 3, respectively.…”

Section: Development Of An Algorithm For Indexing Heterogeneous Datamentioning

confidence: 99%

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

Zuiev¹,

Zhyvotovskyi²,

Zvieriev³

et al. 2020

EEJET

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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

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“…These circumstances lead to recourse to the theory of expert systems, where one of the important limitations in their use is the difficulty of formulating rules for machining. In this form, an effective methodology for recording, storing and using expert knowledge in the expert system for the operational sampling of knowledge should be developed [4].…”

Section: Introductionmentioning

confidence: 99%

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

Salnikova¹,

Cherviakova²,

Sova³

et al. 2020

EEJET

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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

show abstract

Processing of signal information in problems of monitoring information security of unmanned autonomous objects

Cited by 15 publications

References 12 publications

Development of an Advanced Method of Finding Solutions for Neuro-Fuzzy Expert Systems of Analysis of the Radioelectronic Situation

Development of an Advanced Method of Finding Solutions for Neuro-Fuzzy Expert Systems of Analysis of the Radioelectronic Situation

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

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

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