Application of Nonlinear Neural Network Model for Self Sensing Characteristic in an Ionic Polymer Metal Composite (IPMC) Actuator

被引：0
|
作者：
Doan, Ngoc Chi Nam [1 ]
Ahn, Kyoung Kwan [1 ]
Dinh, Quang Truong [1 ]
Yoon, Jong Il [1 ]
机构：
[1] Univ Ulsan, Sch Mech & Automot Engn, Ulsan 680749, South Korea
来源：
ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2011, PT II | 2011年 / 6792卷
关键词：
IPMC; Identification; NBBM; RMLPNN; SALM;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper focuses on a self sensing characteristic of an Ionic Polymer Metal Composite (IPMC) bases on a novel accurate nonlinear black-box model (NBBM) to estimate the IPMC tip displacement. The NBBM is formed by a recurrent multi-layer perceptron neural network (RMLPNN) and a self-adjustable learning mechanism (SALM). The model parameters are optimized by using a set of training data. The ability of NBBM model is evaluated by a comparison of the estimated and real IPMC bending characteristic.
引用
收藏
页码：229 / 236
页数：8
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