報 告 人：孫中波 博士

主 持 人：張曉穎

時    間：2019年9月6日15：00-16：00

地    點：第三教學樓五樓大數據實驗室

主辦單位：理學院

報告人簡介：孫中波，博士后（師從任露泉院士），碩士生導師，現為吉林省政府發展研究中心智庫專家、吉林省軟科學研究所智庫專家、中國自動化學會會員、吉林省自動化學會理事、吉林省人工智能學會會員。一直從事下肢康復機器人設計、控制與優化，復雜系統建模、控制與優化等研究工作。主持/參加相關科研項目18項，其中主持國家自然科學基金面上項目1項，中國博士后基金（特助）1項，中國博士后基金（面上）1項，教育部“春暉計劃”項目1項，吉林省科技廳技術攻關項目1項，吉林省教育廳項目1項，參加國家重大科技研發計劃1項，國家自然科學基金3項，省部級項目8項。以第一作者發表與本項目相關的SCI、EI檢索論文20余篇，其中SCI檢索論文10篇，EI檢索論文17篇，核心期刊9篇，在科學出版社出版學術專著1部，授權國家發明專利1件，申請國家發明專利2件。

觀點綜述：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.