The site presents the development of the author in collaboration with the team of the laboratory in the field of neural network technologies and oxide electronics, as well as other developments of the author in the field of education and automation.
In this video, I will tell about the algorithm for calculating simple feedforward neural network, and about the backpropagation of errors algorithm for neural network training. The simplest neural network circuit is shown in the figure. The network has an input layer, which receives data x1, x2, x3, one hidden layer, and the output layer. […]
Read MoreChoose a color scheme:White numbers on a black backgroundBlack numbers on a white backgroundRed numbers on a white background Click on the image of the digit to download the SVG image file. Use Google Chrome browser for the best view. The first 100 images of the training set of handwritten digits from the MNIST database […]
Read MoreWe implement a capacitorless model of a VO2 oscillator by introducing into the circuit of a field-effect transistor and a VO2 thermal sensor, which provide negative current feedback with a time delay. We compare the dynamics of current and voltage oscillations on a switch in a circuit with a capacitor and without a capacitor. The […]
Read Moreby Petr Boriskov and Andrei Velichko Electronics2019, 8(9), 922; https://doi.org/10.3390/electronics8090922 – 22 Aug 2019 The circuits, presented in the article, are implemented in the MathCAD and LTSpice software packages, and source files are available in Supplementary Materials.
Read MoreThis paper presents a new method for evaluating the synchronization of quasi-periodic oscillations of two oscillators, termed “chimeric synchronization”. The family of metrics is proposed to create a neural network information converter based on a network of pulsed oscillators. In addition to transforming input information from digital to analogue, the converter can perform information processing […]
Read MoreIn 2018, we conducted theoretical and experimental research activities on the methods development for oscillatory neural networks’ operations (networks training), for such applications as pattern storing and pattern recognition, information coding. The synchronization effects in coupled oscillators’ arrays were investigated in details, using a special family of synchronization estimation metrics; and the effects of thermal […]
Read MoreElectronics 2019, 8(1), 75; https://doi.org/10.3390/electronics8010075 Abstract The current study uses a novel method of multilevel neurons and high order synchronization effects described by a family of special metrics, for pattern recognition in an oscillatory neural network (ONN). The output oscillator (neuron) of the network has multilevel variations in its synchronization value with the reference oscillator, and allows […]
Read MoreThe effect of the time delay of thermal coupling on the synchronization of two VO2-oscillators has been studied for the first time. A modification of the methods for determining high order synchronization parameters has been proposed. The dependence of these parameters on the magnitude of the time delay and distance between the oscillators in the […]
Read MorePlease follow the link and read the full text of the article published in the journal Electronics. Development of neuromorphic systems based on new nanoelectronics materials and devices is of immediate interest for solving the problems of cognitive technology and cybernetics. Computational modelling of two- and three-oscillator schemes with thermally coupled VO2-switches is used to […]
Read MoreThe advantage of the thermal mode of interaction of oscillators for 3D integration of VO2 switches is that thermal waves propagate radially from the switch in all directions over the substrate, and therefore allow thermally coupling oscillators located on different layers. Velichko, A.; Belyaev, M.; Putrolaynen, V.; Perminov, V.; Pergament, A. Thermal coupling and effect […]
Read More