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Corrigendum to “Estimation of Acceleration Amplitude of Vehicle by Back Propagation Neural Networks”

Corrigendum to “Estimation of Acceleration Amplitude of Vehicle by Back Propagation Neural Networks” Hindawi Advances in Acoustics and Vibration Volume 2020, Article ID 3527826, 1 page https://doi.org/10.1155/2020/3527826 Corrigendum Corrigendum to “Estimation of Acceleration Amplitude of Vehicle by Back Propagation Neural Networks” 1 2 Mohammad Heidari and Hadi Homaei Mechanical Engineering Group, Islamic Azad University, Aligudarz Branch, P.O. Box 159, Aligudarz, Iran Faculty of Engineering, University of Shahrekord, P.O. Box 115, Shahrekord, Iran Correspondence should be addressed to Mohammad Heidari; moh104337@yahoo.com Received 31 December 2018; Accepted 22 April 2019; Published 4 April 2020 Copyright © 2020 Mohammad Heidari and Hadi Homaei. &is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. &e article titled “Estimation of Acceleration Amplitude References of Vehicle by Back Propagation Neural Networks” [1] [1] M. Heidari and H. Homaei, “Estimation of acceleration am- was found to contain material from Yildirim and Uzmay plitude of vehicle by back propagation neural networks,” [2], which was cited. Details of the similarity are as Advances in Acoustics and Vibration, vol. 2013, Article ID follows: 614025, 7 pages, 2013. (i) In the Abstract, the text “&is paper investigates the [2] S¸. Yildirim and I. Uzmay, “Neural network applications to vehicle’s vibration analysis,” Mechanism and Machine +eory, variation . . . waved stone block paved, and country vol. 38, no. 1, pp. 27–41, 2003. roads” and the sentence “&is method is concep- tually straightforward, and it is also applicable to other type vehicles for practical purposes.” (ii) In the Introduction, the text “Recently, improving comfort and safety conditions . . . by random theory based on statistics” and most of the text in “A solving method of low-frequency vehicle. . . paper is concluded with Section 6.” (iii) In “Random Vibration &eory,” most of the text. &e authors clarified that their article differs from Yil- dirim and Uzmay. In Table 2, the authors used nine methods of training for the BP neural network, while Yildirim and Uzmay used only a radial basis function in this area. &e input of Figure 3 includes four parameters (velocity, damping ratio, the natural frequency of vehicle shock ab- sorber, and road condition) but in Yildirim and Uzmay there is only one parameter (velocity). In the BP model, the authors used three functions (newff, newcf, and newelm) for the first time in this area. Addi- tionally, Yildirim and Uzmay only used a Gaussian function in the hidden layer of RBFNN, while the authors used three activation functions in Table 3. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Advances in Acoustics and Vibration Hindawi Publishing Corporation

Corrigendum to “Estimation of Acceleration Amplitude of Vehicle by Back Propagation Neural Networks”

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Publisher
Hindawi Publishing Corporation
Copyright
Copyright © 2020 Mohammad Heidari and Hadi Homaei. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
ISSN
1687-6261
eISSN
1687-627X
DOI
10.1155/2020/3527826
Publisher site
See Article on Publisher Site

Abstract

Hindawi Advances in Acoustics and Vibration Volume 2020, Article ID 3527826, 1 page https://doi.org/10.1155/2020/3527826 Corrigendum Corrigendum to “Estimation of Acceleration Amplitude of Vehicle by Back Propagation Neural Networks” 1 2 Mohammad Heidari and Hadi Homaei Mechanical Engineering Group, Islamic Azad University, Aligudarz Branch, P.O. Box 159, Aligudarz, Iran Faculty of Engineering, University of Shahrekord, P.O. Box 115, Shahrekord, Iran Correspondence should be addressed to Mohammad Heidari; moh104337@yahoo.com Received 31 December 2018; Accepted 22 April 2019; Published 4 April 2020 Copyright © 2020 Mohammad Heidari and Hadi Homaei. &is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. &e article titled “Estimation of Acceleration Amplitude References of Vehicle by Back Propagation Neural Networks” [1] [1] M. Heidari and H. Homaei, “Estimation of acceleration am- was found to contain material from Yildirim and Uzmay plitude of vehicle by back propagation neural networks,” [2], which was cited. Details of the similarity are as Advances in Acoustics and Vibration, vol. 2013, Article ID follows: 614025, 7 pages, 2013. (i) In the Abstract, the text “&is paper investigates the [2] S¸. Yildirim and I. Uzmay, “Neural network applications to vehicle’s vibration analysis,” Mechanism and Machine +eory, variation . . . waved stone block paved, and country vol. 38, no. 1, pp. 27–41, 2003. roads” and the sentence “&is method is concep- tually straightforward, and it is also applicable to other type vehicles for practical purposes.” (ii) In the Introduction, the text “Recently, improving comfort and safety conditions . . . by random theory based on statistics” and most of the text in “A solving method of low-frequency vehicle. . . paper is concluded with Section 6.” (iii) In “Random Vibration &eory,” most of the text. &e authors clarified that their article differs from Yil- dirim and Uzmay. In Table 2, the authors used nine methods of training for the BP neural network, while Yildirim and Uzmay used only a radial basis function in this area. &e input of Figure 3 includes four parameters (velocity, damping ratio, the natural frequency of vehicle shock ab- sorber, and road condition) but in Yildirim and Uzmay there is only one parameter (velocity). In the BP model, the authors used three functions (newff, newcf, and newelm) for the first time in this area. Addi- tionally, Yildirim and Uzmay only used a Gaussian function in the hidden layer of RBFNN, while the authors used three activation functions in Table 3.

Journal

Advances in Acoustics and VibrationHindawi Publishing Corporation

Published: Apr 4, 2020

References