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Документ Analysis Of Modeling Methods Of Computer Engineering Digital Devices(Ninety Nine Publication, 2021) Dmitrienko, V. D.; Zakovorotnyi, A. Yu. ; Leonov, S. Yu.When designing new digital instruments and devices, there are a vast variety of reasons why the finally designed devices will malfunction. To decrease the number of such failures and to increase design accuracy, various methods and systems for modeling digital devices are used. In these systems, various methods for describing signals in models of designed devices can be used. In this case, three-valued, four-valued, ..., nine-valued, thirteen-valued, as well as analog signal descriptions can be applied. Increasing signal and element models complexity in digital devices allows designing more accurate models. However, when modeling digital devices, multi-valued alphabets do not allow to increase the accuracy of modeling and research of dynamic processes in devices. This is due to the impossibility of taking into account processes and interference caused by both stray capacitances and inductances between separate components of the devices and conductors connecting them, as well as dynamic processes, caused by external electromagnetic fields affecting the device designed. Describing such processes using continuous or K-valued differential equations improves the accuracy of digital devices modeling. Nevertheless, the problems of automated testing of these devices and the automation of determining their performance remain unsolved. For automated recognition of failures in the designed digital devices, neural networks, in particular, adaptive resonant theory (ART) neural networks, can be applied, since they have an important property, the ability to retrain when additional information about failures occurs. However, neural networks also have an essential drawback: they do not allow getting more than one solution, although with K-valued differential calculus of digital devices, this can occur quite often, which makes it possible to recognize failures that can be attributed simultaneously to two or more different classes of errors, and, therefore, to recognize failures, which can be simultaneously assigned to two or more different classes, and consequently, get more accurate results. In this regard, it is necessary to develop neural networks that could recognize two or more possible solutions (or types of failures). This would expand the field of failures automated detection in the designed digital devices and determine the performance accuracy. Figs.: 3. Refs: 12 titles.Документ Neural networks art: solving problems with multiple solutions and new teaching algorithm(2014) Dmitrienko, V. D.; Zakovorotnyi, A. Yu.; Leonov, S. Yu.; Khavina, I. P.A new discrete neural networks adaptive resonance theory (ART), which allows solving problems with multiple solutions, is developed. New algorithms neural networks teaching ART to prevent degradation and reproduction classes at training noisy input data is developed. Proposed learning algorithms discrete ART networks, allowing obtaining different classification methods of input.Документ The neural network art which uses the Hamming distance to measure an image similarity score(Asian Research Publishing Networ, 2019) Dmitrienko, V. D.; Zakovorotnyi, A. Yu.; Leonov, S. Yu.This study reports a new discrete neural network of Adaptive Resonance Theory (ART-1H) in which the Hamming distance is used for the first time to estimate the measure of binary images (vectors) proximity. For the development of a new neural network of adaptive resonance theory, architectures and operational algorithms of discrete neural networks ART-1 and discrete Hamming neural networks are used. Unlike the discrete neural network adaptive resonance theory ART-1 in which the similarity parameter which takes into account single images components only is used as a measure of images (vectors) proximity in the new network in the Hamming distance all the components of black and white images are taken into account. In contrast to the Hamming network, the new network allows the formation of typical vector classes representatives in the learning process not using information from the teacher which is not always reliable. New neural network can combine the advantages of the Hamming neural network and ART-1 by setting a part of source information in the form of reference images (distinctive feature and advantage of the Hamming neural network) and obtaining some of typical image classes representatives using learning algorithms of the neural network ART-1 (the dignity of the neural network ART-1). The architecture and functional algorithms of the new neural network ART which has the properties of both neural network ART-1 and the Hamming network were proposed and investigated. The network can use three methods to get information about typical image classes representatives: teacher information, neural network learning process, third method uses a combination of first two methods. Property of neural network ART-1 and ART-1H, related to the dependence of network learning outcomes or classification of input information to the order of the vectors (images) can be considered not as a disadvantage of the networks but as a virtue. This property allows to receive various types of input information classification which cannot be obtained using other neural networks.