Prediction of Particle Damping Parameters using RBF Neural Network

被引：9
|
作者：
Veeramuthuvel, P. [1 ,2 ]
Shankar, K. [2 ]
Sairajan, K. K. [1 ]
Machavaram, Rajendra [2 ]
机构：
[1] ISRO Satellite Ctr, Dept Space, Bangalore 560017, Karnataka, India
[2] IIT Madras, Machine Design Sect, Dept Mech Engn, Madras 600036, Tamil Nadu, India
来源：
INTERNATIONAL CONFERENCE ON ADVANCES IN MANUFACTURING AND MATERIALS ENGINEERING (ICAMME 2014) | 2014年 / 5卷
关键词：
Particle Damping; Damping Ratio; Back Propagation Network; Radial Basis Function; System Parameters;
D O I：
10.1016/j.mspro.2014.07.275
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Particle damping is one of the recent passive damping methods used for effective vibration suppression. This paper discusses two different Artificial Neural Networks - Feed Forward Back Propagation Network and Radial Basis Function - applied to determine the relationship between the damping ratio and system parameters based on extensive experiments carried out on an aluminium alloy beam. The experiments are carried out with different combinations of system parameters for the estimation of damping ratio. Based on the Neural Network predictions, the factors which affect the damping performances are studied in detail for the given combination of system parameters. (C) 2014 Elsevier Ltd.
引用
收藏
页码：335 / 344
页数：10
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