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个人简介

项目情况: 1、国家自然科学基金青年项目,61603143,冗余度机械臂加速度层避障规划设计与分析,2017.01-2019.12,21万元,在研,主持 2、福建省自然科学基金面上项目,2016J01307,冗余度机械臂加速度规划的新技术及理论分析,2016.04-2019.04,5万元,在研,主持 3、华侨大学中青年教师科技创新资助计划项目,ZQN-YX402,求解时变问题新方法的设计、开发及应用研究,2016.10-2020.09,80万元,在研,主持 4、2016年大学生创新创业训练计划项目,国家级,时变非线性优化问题求解的新算法及应用研究,1万元,在研,指导教师

研究领域

主要从事机器人控制、神经网络、数值算法、极限学习机器等研究工作。

近期论文

查看导师最新文章 (温馨提示:请注意重名现象,建议点开原文通过作者单位确认)

1. Dongsheng Guo, Zhuoyun Nie, and Laicheng Yan, “Novel discrete-time Zhang neural network for time-varying matrix inversion”, IEEE Transactions on Systems, Man, and Cybernetics: Systems, in press, 2017. 2. Dongsheng Guo, Xinjie Lin, Zhaozhu Su, Sibo Sun, and Zhijing Huang, “Design and analysis of two discrete-time ZD algorithms for time-varying nonlinear minimization”, Numerical Algorithms, in press, 2017. 3. Dongsheng Guo, Zhuoyun Nie, and Laicheng Yan, “Theoretical analysis, numerical verification and geometrical representation of new three-step DTZD algorithm for time-varying nonlinear equations solving”, vol. 214, pp. 516-526, 2016. 4. Dongsheng Guo and Yunong Zhang, “ZNN for solving online time-varying linear matrix-vector inequality via equality conversion”, Applied Mathematics and Computation, vol. 259, pp. 327-338, 2015. 5. Dongsheng Guo, Yunong Zhang, Zhengli Xiao, Mingzhi Mao, and Jianxi Liu, “Common nature of learning between BP-type and Hopfield-type neural networks”, Neurocomputing, vol. 167, pp. 578-586, 2015. 6. Dongsheng Guo and Yunong Zhang, “Acceleration-level inequality-based MAN scheme for obstacle avoidance of redundant robot manipulators”, IEEE Transactions on Industrial Electronics, vol. 61, no. 12, pp. 6903-6914, 2014. 7. Dongsheng Guo and Yunong Zhang, “Zhang neural network for online solution of time-varying linear matrix inequality aided with an equality conversion”, IEEE Transactions on Neural Networks and Learning Systems, vol. 25, no. 2, pp. 370-382, 2014. 8. Dongsheng Guo and Yunong Zhang, “Simulation and experimental verification of weighted velocity and acceleration minimization for robotic redundancy resolution”, IEEE Transactions on Automation Science and Engineering, vol. 11, no. 4, pp. 1203-1217, 2014.

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