检索结果-南通市图书馆

L_2-L_∞ learning of dynamic neural networks

Chinese Physics B 2010年第10期19卷 1-6页

作者： Choon Ki Ahn Department of Automotive Engineering Seoul National University of Technology 172 Gongneung 2-dong Nowon-gu Seoul 139-743 Korea

This paper proposes an y2-y∞ learning law as a new learning method for dynamic neural networks with external disturbance. Based on linear matrix inequality （LMI） formulation, the y2-y∞ learning law is presented to not only guarantee asymptotical stability of dynamic neural networks but also reduce the effect of external disturbance to an y2-y∞ induced norm constraint. It is shown that the design of the y2-y∞ learning law for such neural networks can be achieved by solving LMIs, which can be easily facilitated by using some standard numerical packages. A numerical example is presented to demonstrate the validity of the proposed learning law.

关键词： y2-y∞ learning law dynamic neural networks linear matrix inequality Lyapunovstability theory

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Recursive recurrent neural network:A novel model for manipulator control with different levels of physical constraints

引用

CAAI Transactions on Intelligence Technology 2023年第3期8卷 622-634页

作者： Zhan Li Shuai Li Department of Computer Science Swansea UniversitySwanseaUK College of Engineering Swansea UniversitySwanseaUK

Manipulators actuate joints to let end effectors to perform precise path tracking *** neural network which is described by dynamic models with parallel processing capability,is a powerful tool for kinematic control of *** to physical limitations and actuation saturation of manipulator joints,the involvement of joint constraints for kinematic control of manipulators is essential and ***,current existing manipulator control methods based on recurrent neural networks mainly handle with limited levels of joint angular constraints,and to the best of our knowledge,methods for kinematic control of manipulators with higher order joint constraints based on recurrent neural networks are not yet *** this study,for the first time,a novel recursive recurrent network model is proposed to solve the kinematic control issue for manipulators with different levels of physical constraints,and the proposed recursive recurrent neural network can be formulated as a new manifold system to ensure control solution within all of the joint constraints in different *** theoretical analysis shows the stability and the purposed recursive recurrent neural network and its convergence to *** results further demonstrate the effectiveness of the proposed method in end‐effector path tracking control under different levels of joint constraints based on the Kuka manipulator *** with other methods such as the pseudoinverse‐based method and conventional recurrent neural network method substantiate the superiority of the proposed method.

关键词： dynamic neural networks recursive computation robotic manipulator

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Design and performance analysis of tracking controller for uncertain nonlinear composite system using neural networks

引用

控制理论与应用（英文版） 2005年第2期3卷 110-116页

作者： Endong LIU Yuanwei JING Siying ZHANG School of lnformation Sciences ＆ Engineering Northeastern University Shenyang Liaoning 110004 China The Hot Rolled Strip Plant Angang New Iron and Steel Company Anshan Liaoning 114006 China

Based on high order dynamic neural network, this paper presents the tracking problem for uncertain nonlinear composite system, which contains external disturbance, whose nonlinearities are assumed to be unknown. A smooth controller is designed to guarantee a uniform ultimate boundedness property for the tracking error and all other signals in the dosed loop. Certain measures are utilized to test its performance. No a priori knowledge of an upper bound on the “optimal” weight and modeling error is required; the weights of neural networks are updated on-line. Numerical simulations performed on a simple example illustrate and clarify the approach.

关键词： Uncertain nonlinear composite system dynamic neural networks Adaptive control Performance

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：