Neural network based posture control of a human arm model in the sagittal plane

被引：0

作者：

Liu, Shan ^{[1
]}

Wang, Yongji ^{[1
]}

Huang, Jian ^{[1
]}

机构：

[1] Huazhong Univ Sci & Technol, Dept Control Sci & Engn, Wuhan 430074, Hubei, Peoples R China

来源：

ADVANCES IN NEURAL NETWORKS - ISNN 2006, PT 3, PROCEEDINGS | 2006年 / 3973卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper posture control of a human arm in the sagittal plane is investigated by means of model simulations. The arm is modeled by a nonlinear neuromusculoskeletal model with two degrees of freedom and six muscles. A multilayer perceptron network is used in this paper, and effectively adapted by Levenberg-Marquardt training algorithm. The duration of next movement is regulated according as current feedback states. Simulation Results indicate that this method can maintain two joints at different location in allowable bound. The control scheme provides novel insight into neural prosthesis control and robotic control.

引用

页码：792 / 798

页数：7

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