时间：2018年4月24日10:30-11:00
地点：旗山校区数信大楼507学术报告厅
主讲：吉首大学 肖 林 副教授
主办：数学与信息学院
参加对象：相关研究领域的老师和信息类学生
专家简介：肖林，男，1986年7月生，2014年6月获得中山大学博士学位，现任职于吉首大学，职称（或职务）为副教授，在香港理工大学访学。主要学术业绩为：在主流期刊和会议发表（含录用）学术论文60余篇，SCI期刊论文38篇，IEEE Transactions系列论文8篇（影响因子累计为110.219），CRC专著一部；授权发明专利、实用新型专利和计算机软件著作权各1项；主持国家自然科学基金青年项目、湖南省自然科学基金面上项目和湖南省教育厅优秀青年项目各一项。
报告摘要：Inspired by the negative impact of additive noises on zeroing neural network (ZNN) for time-varying problems, a unified zeroing neural network (UZNN) is designed and presented to achieve noise suppression and finite-time convergence simultaneously. Compared to the existing ZNN model only with finite-time convergence, the proposed UNNN model inherently possesses the extra robustness property in front of additive noises, in addition to finite-time convergence. Furthermore, the design process, theoretical analysis, and numerical verification of the proposed UZNN model are supplied in details. Both theoretical and numerical results demonstrate the better property of the proposed UZNN model for solving time-varying problems in the presence of additive noises, as compared with the ZNN model.
