Модифицированная нейронная сеть Элмана с динамическим показателем обучения для отслеживания и прогнозирования движения неголономного трехколесного мобильного робота
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Научный журнал Моделирование, оптимизация и информационные технологииThe scientific journal Modeling, Optimization and Information Technology
Online media
issn 2310-6018

Modified Elman neural network with dynamic learning rate for tracking and motion prediction of a nonholonomic three-wheeled mobile robot

Berezina V.A.   idMezentseva O.S. Mezentsev D.V.  

UDC 004.021
DOI: 10.26102/2310-6018/2022.38.3.003

  • Abstract
  • List of references
  • About authors

This article proposes to track and predict the trajectory of a non-holonomic three-wheeled mobile robot using a modified Elman neural network. An algorithm for calculating the learning rate of a neural network is suggested, which improves the efficiency and speed of learning and also reduces the number of iterations required for learning. The modified Elman algorithm with dynamic learning rate (MENN) is compared with the classical Elman neural network (ENN) and the PSO algorithm (PSO-ENN). Training of a neural network is evaluated according to two criteria: the number of iterations required for training and the average training time. In addition, the deviation from the given trajectory of movement is checked: movement along a straight line, in a square and in a circle for each algorithm. The simulation results showed that the modified Elman neural network with a dynamic learning rate is more efficient (by 32.4% on average) and accomplishes the learning objective faster (by 66.4% on average) and has the least deviation from the given motion trajectory. The relative measurement error ranges from 7.8% to 20.2% at 95% reliability and five tests for each group of measurements.

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Berezina Victoria Andreevna

North-Caucasus Federal University

Stavropol, Russian Federation

Mezentseva Oksana Stanislavovna
Candidate of physical-mathematical sciences

ORCID |

North-Caucasus Federal University

Stavropol, Russian Federation

Mezentsev Dmitry Viktorovich

North-Caucasus Federal University

Stavropol, Russian Federation

Keywords: recurrent neural network, elman neural network, learning rate, nonholonomic three-wheeled robot, motion trajectory prediction

For citation: Berezina V.A. Mezentseva O.S. Mezentsev D.V. Modified Elman neural network with dynamic learning rate for tracking and motion prediction of a nonholonomic three-wheeled mobile robot. Modeling, Optimization and Information Technology. 2022;10(3). Available from: https://moitvivt.ru/ru/journal/pdf?id=1199 DOI: 10.26102/2310-6018/2022.38.3.003 (In Russ).

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Received 01.06.2022

Revised 06.07.2022

Accepted 15.07.2022

Published 19.07.2022