Test Model of Automobile Engine Magneto-Rheological Mount Based on RBF Neural Network

Jin Gang Ma; Peng Cheng Sheng; Hong Ying Song; Chun Lan Liang

doi:10.4028/www.scientific.net/AMM.365-366.450

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 365-366Test Model of Automobile Engine...

Test Model of Automobile Engine Magneto-Rheological Mount Based on RBF Neural Network

Abstract:

The nonlinear and hysteresis characteristics showed by magneto-rheological (MR) mount make it seem very difficult to establish a precise mathematical model. Based on the testing of MR mount dynamics, RBF neural network model can train and forecast the collected data. Analysis of comparing the predicting result of the RBF neural network model with the testing result shows that the trained RBF neural network model can exactly predict the dynamics of MR mount, and it provides some new ideas to implement the better intelligent control of the engine MR mount.

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Periodical:

Applied Mechanics and Materials (Volumes 365-366)

Pages:

450-453

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.365-366.450

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Cite this paper

Online since:

August 2013

Authors:

Jin Gang Ma, Peng Cheng Sheng, Hong Ying Song, Chun Lan Liang

Keywords:

Dynamic, MR Mount, RBF Neural Network, Test Model

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References

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