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Open AccessArticle

A Model of an Oscillatory Neural Network with Multilevel Neurons for Pattern Recognition and Computing

Institute of Physics and Technology, Petrozavodsk State University, 31 Lenina str., 185910 Petrozavodsk, Russia
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Electronics 2019, 8(1), 75; https://doi.org/10.3390/electronics8010075
Received: 20 November 2018 / Revised: 1 January 2019 / Accepted: 5 January 2019 / Published: 9 January 2019
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 classification of an input pattern into a set of classes. The ONN model is implemented on thermally-coupled vanadium dioxide oscillators. The ONN is trained by the simulated annealing algorithm for selection of the network parameters. The results demonstrate that ONN is capable of classifying 512 visual patterns (as a cell array 3 × 3, distributed by symmetry into 102 classes) into a set of classes with a maximum number of elements up to fourteen. The classification capability of the network depends on the interior noise level and synchronization effectiveness parameter. The model allows for designing multilevel output cascades of neural networks with high net data throughput. The presented method can be applied in ONNs with various coupling mechanisms and oscillator topology. View Full-Text
Keywords: oscillatory neural networks; pattern recognition; higher order synchronization; thermal coupling; vanadium dioxide; computing oscillatory neural networks; pattern recognition; higher order synchronization; thermal coupling; vanadium dioxide; computing
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MDPI and ACS Style

Velichko, A.; Belyaev, M.; Boriskov, P. A Model of an Oscillatory Neural Network with Multilevel Neurons for Pattern Recognition and Computing. Electronics 2019, 8, 75.

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