Modeling and Prediction of Vehicle Tube Hydraulic Shock Absorbers Based on BP Neural Network

Author

Pan, Dong ; Pan, Shuang-xia ; Wang, Wei-rui

Author_Institution

Inst. of Mech. Design, Zhejiang Univ., Hangzhou

fYear

2006

fDate

13-16 Aug. 2006

Firstpage

2935

Lastpage

2939

Abstract

Research on modeling the tube hydraulic shock absorbers is always a challenging issue. This paper presents a modeling method through BP (back-propagation) neural network established by training data from experiments. Characteristic parameters of the absorbers are as the inputs of the BP network model, while damping forces as outputs. Numerical simulations are given as examples, which demonstrate that the method is effective to predict the performance of the absorber successfully

Keywords

backpropagation; damping; learning (artificial intelligence); shock absorbers; vehicles; BP neural network; training data; vehicle tube hydraulic shock absorber; Cybernetics; Damping; Machine learning; Mathematical model; Neural networks; Numerical simulation; Pistons; Predictive models; Shock absorbers; Training data; Valves; Vehicles; Vibrations; BP neural network; Shock absorber; model; predict;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2006 International Conference on

Conference_Location

Dalian, China

Print_ISBN

1-4244-0061-9

Type

conf

DOI

10.1109/ICMLC.2006.259141

Filename

4028564

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2895201