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.

scholarly journals Development of a Vision System for an Intelligent Robotic Hand Prosthesis Using Neural Network Technology

2020 ◽  
Vol 35 ◽  
pp. 04006
Author(s):  
Andrew A. Boshlyakov ◽  
Alexander S. Ermakov
Keyword(s):  
Neural Network ◽  
Vision System ◽  
Mathematical Education ◽  
Robotic Hand ◽  
Network Technology ◽  
Technical Vision ◽  
Recognition Of Objects ◽  
Network Technologies ◽  
Neural Network Technologies ◽  
Neural Network Technology

A brief review of the existing auxiliary prosthetic control systems was carried out. The concept of an intelligent prosthesis is proposed, which will expand the possibilities of application and simplify the use of the prosthesis. The required actions of the vision system in automatic and manual capture modes are considered. The sequence of operation of the subsystems of the technical vision system is determined. The possibility of implementing a prosthesis vision system based on neural network technology is shown. The method of using a ready-made neural network for recognition of objects by a prosthesis is considered. The possibilities of using the considered neural network technologies in the mathematical education of engineers are presented. A version of the prosthesis design is proposed. The possibility of constructing the described prosthesis is shown.

System design of neurocomputers on microcontrollers in conditions of limited resources

2021 ◽  
Author(s):  
K.V. Selivanov ◽  
V.S. Klimachev
Keyword(s):  
Neural Network ◽  
Electronic Equipment ◽  
Portable Devices ◽  
Network Technology ◽  
Resource Saving ◽  
Computing Power ◽  
C Language ◽  
Network Technologies ◽  
Neural Network Technologies ◽  
Neural Network Technology

The development of software tools for electronic equipment has led to the development and widespread use of neural network technology. They are used for processing and making decisions based on the received information, which is not discrete, but has a polymorphic essence. Processing entity data and computing decisions requires significant amounts of computing power and device operating memory. This problem does not allow the widespread use of neural network technologies in portable devices and devices based on microcontrollers. The aim of the article – adapt neural network technology for use on portable environments and microcontroller-based electronic devices. The chosen method of implementing a neural network based on the resource-saving Hamming algorithm, and the optimized program code in the C language made it possible to significantly reduce the requirements for the hardware of the device on which this technology can be implemented. The analysis of modern microcontrollers allowed us to choose and apply the optimal power-to-energysaving microcontroller-STM32, which allowed us to implement a simplified neural network on its basis. The developed algorithm was implemented on a debug board with an STM32 microcontroller in a device that allows you to recognize handwritten numbers entered from the touch screen. The created portable device for recognizing handwritten numbers is applicable as a module in other electronic equipment products. The prospects of using the latest variants of implementing neurocomputers on microcontrollers are shown.

Digital Watermarking Based on Neural Network Technology for Grayscale Images

2011 ◽  
pp. 204-212
Author(s):  
Jeanne Chen ◽  
Tung-Shou Chen ◽  
Keh-Jian Ma ◽  
Pin-Hsin Wang
Keyword(s):  
Neural Network ◽  
Digital Watermarking ◽  
The Internet ◽  
Network Technology ◽  
Traditional Methods ◽  
Internet Environment ◽  
Network Technologies ◽  
Data Passing ◽  
Neural Network Technology ◽  
Grayscale Images

Great advancements made on information and network technologies have brought on much activity on the Internet. Traditional methods of trading and communication are so revolutionized that everything is quasi-online. Amidst the rush to be online emerge the urgent need to protect the massive volumes of data passing through the Internet daily. A highly dependable and secure Internet environment is therefore of utmost importance.

scholarly journals Problem-Solving Skills Among Precollege Students in Clinical Immunology and Microbiology: Classifying Strategies with a Rubric and Artificial Neural Network Technology

2001 ◽  
Vol 2 (1) ◽  
pp. 25-33
Author(s):  
SUSAN KANOWITH-KLEIN ◽  
MEL STAVE ◽  
RON STEVENS ◽  
ADRIAN M. CASILLAS
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
High School Students ◽  
Problem Solving ◽  
Network Technology ◽  
Problem Solving Skills ◽  
Problem Solving Strategies ◽  
Artificial Neural ◽  
Neural Network Technology

Educators emphasize the importance of problem solving that enables students to apply current knowledge and understanding in new ways to previously unencountered situations. Yet few methods are available to visualize and then assess such skills in a rapid and efficient way. Using a software system that can generate a picture (i.e., map) of students’ strategies in solving problems, we investigated methods to classify problem-solving strategies of high school students who were studying infectious and noninfectious diseases. Using maps that indicated items students accessed to solve a software simulation as well as the sequence in which items were accessed, we developed a rubric to score the quality of the student performances and also applied artificial neural network technology to cluster student performances into groups of related strategies. Furthermore, we established that a relationship existed between the rubric and neural network results, suggesting that the quality of a problem-solving strategy could be predicted from the cluster of performances in which it was assigned by the network. Using artificial neural networks to assess students’ problem-solving strategies has the potential to permit the investigation of the problem-solving performances of hundreds of students at a time and provide teachers with a valuable intervention tool capable of identifying content areas in which students have specific misunderstandings, gaps in learning, or misconceptions.

APPLICATION OF NEURAL NETWORK TECHNOLOGIES FOR QUALITY MANAGEMENT FOOD PRODUCTS IN THE PRODUCTION PROCESS

2021 ◽  
pp. 37-41
Author(s):  
В.Г. Благовещенский ◽  
А.Е. Краснов ◽  
Е.И. Баженов ◽  
М.М. Благовещенская ◽  
С.А. Мокрушин
Keyword(s):  
Neural Network ◽  
Quality Management ◽  
Production Process ◽  
Automatic System ◽  
Food Products ◽  
Quality Of Food ◽  
Network Technologies ◽  
Neural Network Technologies

Рассматривается задача разработки интеллектуальной автоматизированной системы управления качеством кондитерских изделий с использованием нейросетевых технологий на примере производства подсолнечной халвы. The problem of developing an automatic system for managing the quality of food products using neural network technologies is considered on the example of the production of sunflower halva.

scholarly journals Application of Neural Network Technologies for Inflation Forecasting

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Inflation Rate ◽  
Forecast Model ◽  
Model Construction ◽  
Inflation Forecasting ◽  
Network Technologies ◽  
Neural Network Technologies

The paper considers the methodology of forecasting the level of inflation in Russia with the help of analytical platform Deductor Studio. In solving the problem, the mechanisms of data purification from noises and anomalies were applied, which ensured the quality of forecast model construction and receipt of forecast values for five months in advance. The principle of forecasting the time series was also demonstrated: import, seasonal detection, cleaning, smoothing, construction of forecast model, and forecasting the inflation rate for five months ahead.

The Methodical Complex of Laboratory Works on the Study of Neural Network Technologies

2019 ◽  
pp. 358-367
Author(s):  
Artem Borodkin ◽  
Vladimir Eliseev ◽  
Gennady Filaretov ◽  
Alireza Aghvami Seyed
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Undergraduate Students ◽  
Software Package ◽  
Practical Experience ◽  
Software Packages ◽  
Network Methods ◽  
Network Technologies ◽  
And Control ◽  
Neural Network Technologies

The chapter considers a task of teaching undergraduate students practical skills using artificial neural networks to solve problems of information processing and control systems. It represents and proves the methods of teaching, based on the gradual increase in the complexity of tasks to be solved by students. The developed complex of laboratory works includes classical problems and methods of their solutions, as well as original methods for solving problems of automatic control. The technology base of the laboratory works are both well-known programs and software package developed by the authors. In addition to the practical experience in the use of software packages, students obtain experience in conducting comparative studies of traditional and neural network methods for solving control problems.

ON USING INDICATOR COUNTERS AND NEURAL NETWORK TECHNOLOGIES TO MONITOR THE TECHNICAL CONDITION OF ENERGY-SATURATED TRACTORS UNDER THE LEASE

2021 ◽  
pp. 4-10
Author(s):  
MIKHAIL N. KOSTOMAKHIN ◽  
Keyword(s):  
Neural Network ◽  
Operational Reliability ◽  
Technical Condition ◽  
Automated System ◽  
Diagnostic Tools ◽  
Technical Diagnostics ◽  
Warranty Period ◽  
Network Technologies ◽  
Neural Network Technologies ◽  
Neural Network Technology

Reducing the risks and the equipment ownership costs for the lessor, especially in the post-warranty period, requires developing and implementing a new automated system for equipment maintenance. The article presents an analysis and off ers possibilities of using indicator counters and neural network technologies to monitor the technical condition of energy-rich tractors online. The authors give an example of using a neural network calculator to identify malfunctions in the transmission line and increase the controllability and objectivity of assessing the current technical condition of tractors under the lease. Counters-indicators are built-in express diagnostic tools. Their use helps minimize preparatory operations to determine the technical condition, visualize and analyze parameters for monitoring the technical condition of individual components and units, and increase the operational reliability of equipment under the lease. The use of neural network technology in the technical diagnostics of equipment will generalize the experience of diagnosticians and service experts for fault localization and enable specialists with little experience to assess the technical condition and determine the amount of work needed to eliminate malfunctions, thereby reducing the time and cost of repairs.

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