%0 Journal Article
%T Estimation of Acceleration Amplitude of Vehicle by Back Propagation Neural Networks
%A Mohammad Heidari
%A Hadi Homaei
%J Advances in Acoustics and Vibration
%D 2013
%I Hindawi Publishing Corporation
%R 10.1155/2013/614025
%X This paper investigates the variation of vertical vibrations of vehicles using a neural network (NN). The NN is a back propagation NN, which is employed to predict the amplitude of acceleration for different road conditions such as concrete, waved stone block paved, and country roads. In this paper, four supervised functions, namely, newff, newcf, newelm, and newfftd, have been used for modeling the vehicle vibrations. The networks have four inputs of velocity ( ), damping ratio ( ), natural frequency of vehicle shock absorber ( ), and road condition (R.C) as the independent variables and one output of acceleration amplitude (AA). Numerical data, employed for training the networks and capabilities of the models in predicting the vehicle vibrations, have been verified. Some training algorithms are used for creating the network. The results show that the Levenberg-Marquardt training algorithm and newelm function are better than other training algorithms and functions. This method is conceptually straightforward, and it is also applicable to other type vehicles for practical purposes. 1. Introduction Recently, improveing comfort and safety conditions for vehicles considering disturbances due to road roughness has been studied by several researchers. To minimise the disturbing effects of vibration, optimum damping factor has been investigated. In the case of definite road profile, that is, for the case of definite vibration with single, two, and three degrees of freedom systems, physical and mathematical models can be established. However, in practice, vehicle vibrations arising from road roughness possess random character. Vibration analysis for such systems can be accomplished by random theory based on statistics. A method which can simulate the set vibrations of vehicle has been developed by Guclu and Gulez [1]. In their investigation, neural network control for a nonlinear full vehicle model was defined by using permanent magnet synchronous motor. Chaos and bifurcation in nonlinear vehicle model have been studied by Li et al. [2], Zhu and Ishitobi [3], and Litak et al. [4]. A solving method of low-frequency vehicle vibration problems has been presented by Ishihama et al. [5]. Two ideas have been employed. The first was the phase control on vibration transmission in hydraulic engine methods. The other was the vector synthesis approach in treating multiple vibrations input to the vehicle body. A new method for predicting vibration characteristics of a structure that is considered to undergo a design change has been presented [6]. Methodologies for
%U http://www.hindawi.com/journals/aav/2013/614025/