Algorithm for Solving Ordinary Differential Equations Using Neural Network Technologies

2020 ◽  
pp. 79-92
Author(s):  
Irina Bolodurina ◽  
Denis Parfenov ◽  
Lubov Zabrodina
Keyword(s):  
Neural Network ◽  
Differential Equations ◽  
Ordinary Differential Equations ◽  
Network Technologies ◽  
Neural Network Technologies

МЕТОДЫ ДОСТИЖЕНИЯ МАКСИМАЛЬНОЙ ЭФФЕКТИВНОСТИ ПЛАТФОРМЫ ПРОТОТИПИРОВАНИЯ ВЫСОКОПРОИЗВОДИТЕЛЬНЫХ СИСТЕМ НА КРИСТАЛЛЕ НА ЗАДАЧАХ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА

2020 ◽  
Vol 96 (3s) ◽  
pp. 585-588
Author(s):  
С.Е. Фролова ◽  
Е.С. Янакова
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Computer Vision ◽  
High Performance ◽  
Systems On Chip ◽  
High Performance Systems ◽  
On Chip ◽  
Network Technologies ◽  
Neural Network Technologies

Предлагаются методы построения платформ прототипирования высокопроизводительных систем на кристалле для задач искусственного интеллекта. Изложены требования к платформам подобного класса и принципы изменения проекта СнК для имплементации в прототип. Рассматриваются методы отладки проектов на платформе прототипирования. Приведены результаты работ алгоритмов компьютерного зрения с использованием нейросетевых технологий на FPGA-прототипе семантических ядер ELcore. Methods have been proposed for building prototyping platforms for high-performance systems-on-chip for artificial intelligence tasks. The requirements for platforms of this class and the principles for changing the design of the SoC for implementation in the prototype have been described as well as methods of debugging projects on the prototyping platform. The results of the work of computer vision algorithms using neural network technologies on the FPGA prototype of the ELcore semantic cores have been presented.

scholarly journals Neural network technologies in system synthesis

2021 ◽  
Vol 1047 (1) ◽  
pp. 012099
Author(s):  
O E Filatova ◽  
Yu V Bashkatova ◽  
L S Shakirova ◽  
M A Filatov
Keyword(s):  
Neural Network ◽  
System Synthesis ◽  
Network Technologies ◽  
Neural Network Technologies

scholarly journals RESEARCH OF CLASSIFICATION METHOD OF TV3-117 ENGINE RATINGS OPERATIONS BASED ON NEURAL NETWORK TECHNOLOGIES

2018 ◽  
Vol 0 (4 (6)) ◽  
pp. 93-102
Author(s):  
Юрій Миколайович Шмельов ◽  
Сергій Ігорович Владов ◽  
Олексій Федорович Кришан ◽  
Станіслав Денисович Гвоздік ◽  
Людмила Іванівна Чижова
Keyword(s):  
Neural Network ◽  
Classification Method ◽  
Network Technologies ◽  
Neural Network Technologies

The use of neural network technologies in the field of consumer lending

2021 ◽  
Author(s):  
N.A. Askerova ◽  
◽  
T.D. Sokolov ◽  
A.A. Askerova ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Consumer Lending ◽  
Network Technologies ◽  
Neural Network Technologies

Prospects for the implementation of neural network technologies in radar information processing systems

2021 ◽  
Author(s):  
E.V. Egorova ◽  
A.N. Rybakov ◽  
M.H. Aksyaitov
Keyword(s):  
Neural Network ◽  
Information Processing ◽  
Network Technology ◽  
Network Methods ◽  
External Conditions ◽  
Network Technologies ◽  
Radar Information ◽  
Neural Network Technologies ◽  
Neural Network Technology

Conducted studies of the phased implementation of neural network technologies in the practice of processing radar information, providing for a gradual increase in the level of neural network methods in processing systems, have shown that the use of neural network technologies can improve the quality of radar information processing in the most difficult conditions that require high computing power, when the dynamics of changes in external conditions is very is high and traditional approaches to the creation of processing systems are not able to provide the required level of efficiency. The need to develop theoretical provisions for neural network processing of radar information was revealed, while the main features of information processing in radars determine the relevance of research devoted to preventing the reduction in the quality of radar images in conditions of a large number of targets and a complex «jamming» environment based on the rational use of neural network technology. Analysis of the phased implementation of neural network technologies in radar information processing systems, as well as the use of neural network technology for processing radar information in terms of search and research, makes it possible to increase the efficiency of neural network methods for all processing tasks. Assessment of the required performance of computational tools allows us to single out the main neural network paradigms, the use of which gives a tangible increase in the efficiency of radar information processing, such as multilayer perceptron, Hopfield associative memory and self-organizing Kohonen network, while it is possible to rank the proposed methods in accordance with the required performance, undemanding to computing power and implemented on existing or promising computing facilities with software implementation of neural network paradigms. The analysis of possible directions for improving the quality of radar information processing does not claim to fully cover the entire multifaceted area of such studies. In this paper, only the most universal and widespread neural network paradigms are considered and the main part of possible areas of their application is analyzed. However, the proposed options show that the use of neural network technologies in critical tasks will improve the efficiency of radar information processing for complex, rapidly changing external conditions. The use of the principles of self-learning and the developed apparatus for the synthesis of neural network methods will reduce the duration and complexity of theoretical research, the conduct of which is a necessary and mandatory part of the traditional approach. In the course of further research, some of the proposed methods can be refined, as well as the emergence of new methods that make it possible to more fully use the advantages of neural network technology. Carrying out further research work in these areas will give a powerful stimulating impetus for the creation in the future of highly efficient methods for processing radar information, which can be implemented on the available element base.

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