Представлено результати досліджень з удосконалення методичного апарату порівняльного оцінювання безпілотних авіаційних комплексів у напрямах більш повного врахування складових фінансових витрат і ризикоутворюючих факторів, що супроводжують процеси реалізації проєктів постачання (розроблення) таких комплексів. Як критерій витрат пропонується використовувати вартість життєвого циклу безпілотного авіаційного комплексу. Як критерій успішності реалізації проєктів постачання (розроблення) безпілотних авіаційних комплексів у системі можливих ризиків пропонується використовувати відносний показник долі суми індексів неприпустимих ризиків серед всіх ідентифікованих ризиків, що супроводжують проєкт. Визначено напрями подальших досліджень в частині апробації запропонованого методичного апарату.
We developed a method of training artificial neural networks for intelligent decision support systems. A distinctive feature of the proposed method consists in training not only the synaptic weights of an artificial neural network, but also the type and parameters of the membership function. In case of impossibility to ensure a given quality of functioning of artificial neural networks by training the parameters of an artificial neural network, the architecture of artificial neural networks is trained. The choice of architecture, type and parameters of the membership function is based on the computing resources of the device and taking into account the type and amount of information coming to the input of the artificial neural network. Another distinctive feature of the developed method is that no preliminary calculation data are required to calculate the input data. 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, while making unambiguous decisions. According to the results of the study, this training method provides on average 10–18 % higher efficiency of training artificial neural networks and does not accumulate training errors. This method will allow training artificial neural networks by training the parameters and architecture, determining effective measures to improve the efficiency of artificial neural networks. This method will allow reducing the use of computing resources of decision support systems, developing measures to improve the efficiency of training artificial neural networks, increasing the efficiency of information processing in artificial neural networks.
The subject of the paper is the process of joint use of false aircraft targets as part of a group of combat unmanned aerial vehicles to perform tasks to destroy enemy targets. The current paper determines the optimal number of false aircraft targets in a group of combat unmanned aerial vehicles to defeat targets with the desired degree of their defeat and acceptable losses of own combat unmanned aerial vehicles. The scientific task is to improve the methodology for determining the optimal number of false aircraft targets in a group of combat unmanned aerial vehicles to defeat targets with the desired degree of defeat and acceptable losses of own combat unmanned aerial vehicles. To achieve the purpose of the research paper, the following tasks were performed: the process of joint use of false aircraft targets as part of a group of combat unmanned aerial vehicles to defeat targets with the desired degree of their defeat has been formalized; a mathematical model for determining the optimal composition of false aircraft targets as part of a group of combat unmanned aerial vehicles to minimize the losses of real aircraft during their tasks has been developed; based on the conditions of a practical example, the functioning of the improved methodology has been tested and the relevant recommendations have been substantiated. Methods. The mathematical model uses combinatorics and binomial probability distribution. The following results were obtained. An improved methodology is presented, which is multifunctional since, on the one hand, its use makes it possible to determine the required number of false aircraft targets in a group of combat unmanned aerial vehicles to defeat targets with the desired degree of their defeat and acceptable losses of own combat unmanned aerial vehicles, and on the other hand, to determine the predicted level of losses of real aircraft targets from the group when using a certain number of false aircraft targets. Conclusions. The availability of an improved methodology with ready-made calculation formulas will allow the prediction of possible results of combat use of groups of unmanned aerial vehicles based on the initial parameters and substantiate recommendations on their possible composition.
The purpose of the article is to improve topographical and geodetic work with the help of unmanned aerial vehicles. The study was carried out on a real example of a topographic-geodetic plan of a section of an overhead power line at the Losevo-Eskhar No. 1 section. The authors proposed solutions that made it possible to reduce the cost of working time and to perform production tasks in relation to large objects more efficiently. The novelty of the article is the use of unmanned aerial vehicles in the processing of geodetic surveys. This approach allows faster processing of topographic and geodetic plans of large objects. The relevance of the article lies in the fact that thanks to the approach proposed by the authors, the consumption of working time will decrease. It will also be possible to solve production tasks more efficiently.
Надано результати аналізу групового застосування безпілотних літальних апаратів, у тому числі в спільних бойових порядках з пілотованими. Окреслено особливості такого застосування та напрями досліджень, необхідних для розвитку систем автоматичного керування групами літальних апаратів, тенденцій створення і застосування високошвидкісних безпілотних літальних апаратів літакового типу.
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