Input load identification using a holographic neural network

被引：6
|
作者：
Kozukue, Wakae
Hagiwara, Ichiro
Miyaji, Hideyuki
机构：
[1] Kanagawa Inst Technol, Dept Mech Engn, Atsugi, Kanagawa 2430292, Japan
[2] Tokyo Inst Technol, Dept Mech Sci & Engn, Meguro Ku, Tokyo 1528552, Japan
来源：
INTERNATIONAL JOURNAL OF VEHICLE DESIGN | 2007年 / 43卷 / 1-4期
关键词：
dynamic load; identification; multiple input-output system; neural network; plate; transient response; Wiener filtering theory;
D O I：
10.1504/IJVD.2007.012302
中图分类号：
TH [机械、仪表工业];
学科分类号：
0802 ;
摘要：
A machine generates and undergoes dynamic loads during its operation. These dynamic loads are the main source of vibration and noise. If the dynamic loads can be identified exactly, it will become possible to provide the data effective for the reduction of vibration and noise. However, by the method of identification of dynamic loads of the conventional multiple-input systems, noise has a large influence on accuracy. Thus, in this paper, an identification method based on a neural network is proposed for independent multiple-input loads, and the results of the simulation by using the conventional method and the neural network are shown and compared in detail.
引用
收藏
页码：173 / 183
页数：11
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