Факультет електроніки та інформаційних технологій (ЕлІТ)
Permanent URI for this communityhttps://devessuir.sumdu.edu.ua/handle/123456789/20
Browse
2 results
Search Results
Item Information-extreme machine training system of functional diagnosis system with hierarchical data structure(National University "Zaporizhzhia Polytechnic", 2022) Shelehov, I.V.; Барченко, Наталія Леонідівна; Барченко, Наталья Леонидовна; Barchenko, Nataliia Leonidivna; Прилепа, Дмитро Вікторович; Прилепа, Дмитрий Викторович; Prylepa, Dmytro Viktorovych; Бібик, Мирослав Віталійович; Бибик, Мирослав Витальевич; Bibyk, Myroslav VitaliiovychContext. The problem of information-extreme machine learning of the functional diagnosis system is considered by the example of recognizing the technical state of a laser printer by typical defects of the printed material. The object of the research is the process of hierarchical machine learning of the functional diagnosis system of an electromechanical device. Objective. The main objective is to improve the functional efficiency of machine learning during functional diagnostics system retraining using automatically forming a new hierarchical data structure for an expanded alphabet of recognition classes. Method. A method of information-extreme hierarchical machine learning of the system of functional diagnosis of a laser printer based on typical defects of the printed material is proposed. The method was developed with functional approach of modeling the cognitive processes of natural intelligence, which makes it possible to give the diagnostic system the properties of adaptability under arbitrary initial conditions for the formation of images of printing defects and flexibility during retraining of the system due to an increase in the power of the alphabet of recognition classes. The method is based on the principle of maximizing the amount of information in the process of machine learning. The process of information-extreme machine learning is considered as an iterative procedure for optimizing the parameters of the functioning of the functional diagnostics system according to the information criterion. As a criterion for optimizing machine learning parameters, a modified Kullback’s information measure is considered, which is a functional of the exact characteristics of classification solutions. According to the proposed categorical functional model, an information-extreme machine learning algorithm has been developed based on a hierarchical data structure in the form of a binary decomposition tree. The use of such a data structure makes it possible to split a large number of recognition classes into pairs of nearest neighbors, for which the optimization of machine learning parameters is carried out according to a linear algorithm of the required depth. Results. Information, algorithmic software for the system of functional diagnostics of a laser printer based on images of typical defects in printed material has been developed. The influence of machine learning parameters on the functional efficiency of the system of functional diagnostics of a laser printer based on images of defects in printed material has been investigated. Conclusions. The results of physical modeling have confirmed the efficiency of the proposed method of information-extreme machine learning of the system of functional diagnosis of a laser printer based on typical defects in printed material and can be recommended for practical use. The prospect of increasing the functional efficiency of information-extremal learning of the functional diagnostics system is to increase the depth of machine learning by optimizing additional parameters of the system’s functions, including the parameters of the formation of the input training matrix.Item Information-extreme machine training of on-board recognition system with optimization of RGB-component digital images(National Aerospace University «Kharkiv Aviation Institute», 2021) Naumenko, I.; Мироненко, Микита Ігорович; Мироненко, Никита Игорович; Myronenko, Mykyta Ihorovych; Savchenko, T.The research increases the recognition reliability of ground natural and infrastructural objects by use of an autonomous onboard unmanned aerial vehicle (UAV). An information-extreme machine learning method of an autonomous onboard recognition system with the optimization of RGB components of a digital image of ground objects is proposed. The method is developed within the framework of the functional approach to modeling cognitive processes of natural intelligence at the formation and acceptance of classification decisions. This approach, in contrast to the known methods of data mining, including neuro-like structures, provides the recognition system with the properties of adaptability to arbitrary initial conditions of image formation and flexibility in retraining the system. The idea of the proposed method is to maximize the information capacity of the recognition system in the machine learning process. As a criterion for optimizing machine learning parameters, a modified Kullback information measure was used, this informational criterion is the functionality of exact characteristics. As optimization parameters, the geometric parameters of hyperspherical containers of recognition classes and control tolerances for recognition signs were considered, which played the role of input data quantization levels when transforming the input Euclidean training matrix into a working binary training matrix using admissible transformations of a working training matrix the offered machine learning method allows to adapt the input mathematical description of recognition system to the maximum full probability of the correct classification decision acceptance. To increase the depth of information-extreme machine learning, optimization was conducted according to the information criterion of the weight coefficients of the RGB components of the brightness spectrum of ground object images. The results of physical modeling on the example the recognition of terrestrial natural and infrastructural objects confirm the increase in functional efficiency of information-extreme machine learning of on-board system at optimum in information understanding weight coefficients of RGB-components of terrestrial objects image brightness.