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About

Jinde Cao received the B.S. degree from Anhui Normal University, Wuhu, China, the M.S. degree from Yunnan University, Kunming, China, and the Ph.D. degree from Sichuan University, Chengdu, China, all in mathematics/applied mathematics, in 1986, 1989, and 1998, respectively. He was a Postdoctoral Research Fellow at the Department of Automation and Computer-Aided Engineering, Chinese University of Hong Kong, Hong Kong, China from 2001 to 2002. Professor Cao is an Endowed Chair Professor, the Dean of Science Department and the Director of the Research Center for Complex Systems and Network Sciences at Southeast University (SEU). He is also the Director of the National Center for Applied Mathematics at SEU-Jiangsu of China and the Director of the Jiangsu Provincial Key Laboratory of Networked Collective Intelligence of China. He is also Honorable Professor of Institute of Mathematics and Mathematical Modeling, Almaty, Kazakhstan. Prof. Cao was a recipient of the National Innovation Award of China, Obada Prize and the Highly Cited Researcher Award in Engineering, Computer Science, and Mathematics by Thomson Reuters/Clarivate Analytics. Professor Cao is elected as a member/fellow of several meritbased Academies of Sciences, including, Russian Academy of Sciences, Academia Europaea (Academy of Europe), European Academy of Sciences and Arts, Lithuanian Academy of Sciences, Pakistan Academy of Sciences, African Academy of Sciences. He is also elected as a fellow of IEEE.

Research areas

  • Computer science
  • Mathematics
  • Control theory (sociology)
  • Artificial intelligence
  • Control (management)

Publications (3)

Sorted by most cited.

  1. Existence, Uniqueness and Exponential Stability of Periodic Solution for Discrete-Time Delayed BAM Neural Networks Based on Coincidence Degree Theory and Graph Theoretic Method

    2019

    View DOI
    52 cites
  2. Finite-time synchronization criterion of graph theory perspective fractional-order coupled discontinuous neural networks

    2020

    View DOI
    43 cites
  3. A perspective on graph theory-based stability analysis of impulsive stochastic recurrent neural networks with time-varying delays

    2019

    View DOI
    30 cites