An evolutionary neural network based tracking control of a human arm in the sagittal plane

被引：0

作者：

Liu, Shan ^{[1
]}

Wang, Yongji ^{[1
]}

Zhu, Quanmin ^{[2
]}

机构：

[1] Huazhong Univ Sci & Technol, Dept Control Sci & Engn, Key Lab Image Proc & Intelligent Control, Wuhan 430074, Hubei, Peoples R China

[2] Univ West England, Fac Comp Engn & Math Sci, Bristol BS16 1QY, Avon, England

来源：

ADVANCES IN COMPUTATION AND INTELLIGENCE, PROCEEDINGS | 2007年 / 4683卷

关键词：

trajectory tracking control; evolutionary diagonal recurrent neural network; genetic algorithm; evolutionary program; dynamic back-propagation; musculoskeletal model;

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 trajectory tracking control of a human arm moving in sagittal plane is investigated. The arm is described by a musculoskeletal model with two degrees of freedom and six muscles, and the control signal is applied directly in muscle space. To design the intelligent controller, an evolutionary diagonal recurrent neural network (EDRNN) is integrated with proper performance indices, which a genetic algorithm (GA) and evolutionary program (EP) strategy are effectively combined with the diagonal neural network (DRNN). The hybrid GA with EP strategy is applied to optimize the DRNN structure and a dynamic back-propagation algorithm (DBP) is used for training the network weights. The effectiveness of the control scheme is demonstrated through a simulated case study.

引用

页码：316 / +

页数：3

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