| 引用本文: | 姜春福，余跃庆，刘迎春.冗余度机器人运动模型的神经网络辨识及实现[J].控制理论与应用,2004,21(3):373~378.[点击复制] |
| JIANG Chun-fu, YU Yue-qing, LIU Ying-chun.Kinematic model identification and implementation of redundant robot based on neural networks[J].Control Theory and Technology,2004,21(3):373~378.[点击复制] |
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| 冗余度机器人运动模型的神经网络辨识及实现 |
| Kinematic model identification and implementation of redundant robot based on neural networks |
| 摘要点击 1504 全文点击 1650 投稿时间：2002-08-01 修订日期：2003-07-17 |
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| DOI编号 10.7641/j.issn.1000-8152.2004.3.010 |
| 2004,21(3):373-378 |
| 中文关键词 冗余机器人 神经网络 辨识 |
| 英文关键词 redundant robot neural networks identification |
| 基金项目 国家自然科学基金项目(59975001); 北京市自然科学基金项目(3012003). |
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| 中文摘要 |
| 为提高网络学习速度,提出了一种新的动态神经网络结构——状态延迟输入动态递归神经网络.以德国PowerCubeTM模块化机器人为研究对象,将机器人系统返回的关节位置信息和OPTOTRAK30203维运动测量系统测得的机器人末端位置信息作为神经网络的学习样本,对包含各种影响因素的机器人运动模型进行了辨识,所得结果及误差分析,说明了SDIDRNN在学习能力上的优越性. |
| 英文摘要 |
| In order to increase the computational efficiency of neural networks,a new network model named state delay input dynamical recurrent neural network is presented in this study.This new neural network is also applied to the model identification of PowerCube\+\{TM\} modular robot system.The data of joint positions retrieved from the robot and the position of the end-effector measured by the OPTOTRAK 3020 are used as learning sets for neural network.The learning superiority of the new neural network is illustrated. |