Advances in Acoustics and Vibration
Volume 2020 (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”

Mohammad Heidari¹ and Hadi Homaei²

¹Mechanical Engineering Group, Islamic Azad University, Aligudarz Branch, P.O. Box 159, Aligudarz, Iran
²Faculty 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. 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.

The article titled “Estimation of Acceleration Amplitude of Vehicle by Back Propagation Neural Networks” [1] was found to contain material from Yildirim and Uzmay [2], which was cited. Details of the similarity are as follows:

(i)In the Abstract, the text “This paper investigates the variation … waved stone block paved, and country roads” and the sentence “This method is conceptually 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 Theory,” most of the text.

The authors clarified that their article differs from Yildirim 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. The input of Figure 3 includes four parameters (velocity, damping ratio, the natural frequency of vehicle shock absorber, 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. Additionally, Yildirim and Uzmay only used a Gaussian function in the hidden layer of RBFNN, while the authors used three activation functions in Table 3.

References

M. Heidari and H. Homaei, “Estimation of acceleration amplitude of vehicle by back propagation neural networks,” Advances in Acoustics and Vibration, vol. 2013, Article ID 614025, 7 pages, 2013.

Ş. Yildirim and İ. Uzmay, “Neural network applications to vehicle’s vibration analysis,” Mechanism and Machine Theory, vol. 38, no. 1, pp. 27–41, 2003.