• 2020 (Vol.34)

Neural network model of multisensory detector vehicle presence in the classification zone of charging points

© 2018 O. P. Maslennikov, I. A. Koptelov, D. P. Nikolaev, S. A. Gladilin

Institute for Information Transmission Problems of the RAS (Kharkevich Institute), 127051 Moscow, Bolshoy Karetny per. 19, Russia
National Research University Higher School of Economics, 101000 Moscow, Myasnitskaya ulitsa 20, Russia

Received 05 Mar 2018

The paper considers the issue of event recognition by a fully connected artificial neural network. Input data present by time series, contains information about sensors condition. Using fully connected neural network, not recurrent, requires compression input time series. It is necessary to reduce count of input neurons. In the work is presented compression algorithm. This algorithm is analog run-length encoding algorithm with some changes. Detector, building by neural network was learning on real data from automatic vehicle classifier. The detector has high accuracy.

Key words: fully connected neural networks, time series, detection extended event

DOI: 10.1134/S0235009218030095

Cite: Maslennikov O. P., Koptelov I. A., Nikolaev D. P., Gladilin S. A. Neirosetevye modeli multisensornogo detektora prisutstviya transportnogo sredstva v zone klassifikatsii punkta vzimaniya platy [Neural network model of multisensory detector vehicle presence in the classification zone of charging points]. Sensornye sistemy [Sensory systems]. 2018. V. 32(3). P. 246-252 (in Russian). doi: 10.1134/S0235009218030095

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