报告题目：基于忆阻的类脑智能电路分析与设计

报告简介：

近年来，受人脑工作机制启发，发展类脑智能逐渐成为人工智能与计算科学领域研究的热点，作为其重要分支的类人情感研究也受到越来越多研究者的重视。情感在我们日常生活中起着至关重要的作用，人与人之间的交流传递着大量的情感信息，它们在以人为中心的环境中对个体的决策、学习、交流和记忆等能力有着关键的影响，同时情感能力也是体现人类智能的重要标志。在此背景下，基于忆阻这一可实现存算一体化的新型纳米级记忆元件，以及人脑情感形成的相关生物学理论，在类脑智能电路分析与设计方面做了相关研究，包括：基于类皮肤感觉处理器的感觉-情感转化电路、多联想情感学习电路、基于人眼状态的疲劳驾驶检测电路，基于忆阻的联想记忆等，希望能够在未来应用于情感机器人的“大脑”中，使之能为我们提供智能化的工作帮助。

报告人：曾志刚，教授，国家杰出青年科学基金获得者，教育部长江学者特聘教授，万人计划科技创新领军人才，图像信息处理与智能控制教育部重点实验室主任，华中科技大学人工智能与自动化学院院长。2003年6月在华中科技大学获系统分析与集成博士学位。曾在香港中文大学和中国科技大学从事博士后研究。先后担任IEEE Transactions on Neural Networks；IEEE Transactions on Cybernetics；IEEE Transactions on Fuzzy Systems；Cognitive Computation；Neural Networks；Applied Soft Computing；自动化学报；控制工程；系统工程与电子技术；控制理论与应用的编委。曾获湖北省自然科学一等奖、湖北省科技进步一等奖、教育部高等学校科学研究优秀成果奖自然科学奖一等奖、国家科学技术进步奖二等奖各一次。

报告题目：Distributed Algorithms for Constrained Optimization over Directed Networks with Link Failure

报告简介：

This talk focuses on distributed convex optimization problems over an unbalanced directed multi-agent network with inequality constraints. The goal is to cooperatively minimize the sum of all locally known convex cost functions. Every single agent in the network only knows its local objective function and local inequality constraint, and is constrained to a privately known convex set. To collaboratively solve the optimization problem, we mainly concentrate on an epigraph form of the original constrained optimization to overcome the unbalancedness of directed networks, and propose a new distributed asynchronous broadcast-based optimization algorithm. The algorithm allows that not only the updates of agents are asynchronous in a distributed fashion, but also the step-sizes of all agents are uncoordinated. An important characteristic of the proposed algorithm is to cope with the constrained optimization problem in the case of unbalanced directed networks whose communications are subjected to possible link failures.

报告人：Tingwen Huang is a Professor at Texas A&M University at Qatar， an IEEE Fellow. He received his B.S. degree from Southwest Normal University (now Southwest University), China, 1990, his M.S. degree from Sichuan University, China, 1993, and his Ph.D. degree from Texas A&M University, College Station, Texas, 2002. After graduated from Texas A&M University, he worked as a Visiting Assistant Professor there. Then he joined Texas A&M University at Qatar (TAMUQ) as an Assistant Professor in August 2003, then he was promoted to Professor in 2013. Dr. Huang’s research areas include neural networks, chaotic dynamical systems, compl3ex networks, optimization and control, smart grid. He was named the Highly Cited Researcher by Clarivate Analytics (2018, 2019). One of his National Priority Research Project was awarded the Best Research Project by Qatar National Research Fund in 2015. Currently, he is the President of Asia Pacific Neural Networks Society (2020)

报告时间：2020年11月05日星期四下午

报告地点：腾讯会议，ID：889 451 964

主办单位：扬州大学数学科学学院

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