报 告 人：孙中波 博士
主 持 人：张晓颖
时 间：2019年9月6日15：00-16：00
地 点：第三教学楼五楼大数据实验室
主办单位：理学院

报告人简介：孙中波，博士后（师从任露泉院士），硕士生导师，现为吉林省政府发展研究中心智库专家、吉林省软科学研究所智库专家、中国自动化学会会员、吉林省自动化学会理事、吉林省人工智能学会会员。一直从事下肢康复机器人设计、控制与优化，复杂系统建模、控制与优化等研究工作。主持/参加相关科研项目18项，其中主持国家自然科学基金面上项目1项，中国博士后基金（特助）1项，中国博士后基金（面上）1项，教育部“春晖计划”项目1项，吉林省科技厅技术攻关项目1项，吉林省教育厅项目1项，参加国家重大科技研发计划1项，国家自然科学基金3项，省部级项目8项。以第一作者发表与本项目相关的SCI、EI检索论文20余篇，其中SCI检索论文10篇，EI检索论文17篇，核心期刊9篇，在科学出版社出版学术专著1部，授权国家发明专利1件，申请国家发明专利2件。

观点综述：The zeroing neural network models for online solving time varying full-rank Moore-Penrose inversions are redesigned and analyzed from a control theoretical framework. To solve time-varying full-rank Moore-Penrose inverse problems with different noises in real time, some modified zeroing neural network models are developed, analyzed and investigated from the perspective of control. Furthermore, the proposed zeroing neural network models globally converge to the theoretical solution of the full-rank Moore-Penrose inverse problem without noises, and exponentially converge to the exact solution in the presence of noises, which are demonstrated theoretically. Moreover, in comparison with existing models, numerical simulations are provided to substantiate the feasibility and superiority of the proposed modified neural network for online solving time-varying full-rank Moore-Penrose problems with inherent tolerance to noises. In addition, the numerical results infer that different activation functions can be applied to accelerate the convergence speed of the zeroing neural network model. Finally, the proposed zeroing neural network models are applied to the motion generation of redundant robot manipulators, which illustrates its high efficiency and robustness.