Recurrent neural network control of functional electrical stimulation systems

被引：0

作者：

Yilei, Wu ^{[1
]}

Qing, Song ^{[1
]}

Xulei, Yang ^{[1
]}

Li, Lan ^{[1
]}

机构：

[1] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore

来源：

2006 INTERNATIONAL CONFERENCE ON BIOMEDICAL AND PHARMACEUTICAL ENGINEERING, VOLS 1 AND 2 | 2006年

关键词：

recurrent neural networks; functional electrical stimulation; L-2; stability; adaptive training algorithm;

D O I：

暂无

中图分类号：

R914 [药物化学];

学科分类号：

100701 ;

摘要：

In this paper, a recurrent neural network (RNN) controller is proposed for the application of functional electrical stimulation (FES) system, which is a fast developing technique in the area of rehabilitation engineering. With the proposed scheme, the FES system can obtain a better response speed and an improved robustness against disturbance compared to a PID controlled one. Furthermore, L-2-stability of RNN training algorithm is guaranteed via input-output analysis from the nonlinear system theory. Finally based upon a musculoskeletal model, computer simulations are carried out to verify the effectiveness of the theoretical results.

引用

页码：391 / +

页数：2

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