H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Electronics and Electrical Engineering D-index 132 Citations 59,187 983 World Ranking 8 National Ranking 1

Research.com Recognitions

Awards & Achievements

2016 - IEEE Fellow For contributions to the analysis of neural networks

2016 - Member of Academia Europaea

Overview

What is he best known for?

The fields of study he is best known for:

  • Control theory
  • Artificial neural network
  • Artificial intelligence

Jinde Cao mainly focuses on Control theory, Artificial neural network, Exponential stability, Synchronization and Lyapunov function. His biological study spans a wide range of topics, including Memristor and Stability. His studies examine the connections between Artificial neural network and genetics, as well as such issues in Matrix, with regards to Measure.

The study incorporates disciplines such as Equilibrium point, Recurrent neural network, Cellular neural network and Applied mathematics in addition to Exponential stability. As a part of the same scientific family, Jinde Cao mostly works in the field of Synchronization, focusing on Nonlinear system and, on occasion, Finite time. Jinde Cao studied Lyapunov function and Network topology that intersect with Topology.

His most cited work include:

  • Brief paper: A unified synchronization criterion for impulsive dynamical networks (528 citations)
  • Global asymptotic stability of a general class of recurrent neural networks with time-varying delays (502 citations)
  • Global asymptotic and robust stability of recurrent neural networks with time delays (451 citations)

What are the main themes of his work throughout his whole career to date?

Jinde Cao focuses on Control theory, Artificial neural network, Exponential stability, Applied mathematics and Stability. His work on Control theory is being expanded to include thematically relevant topics such as Synchronization. His work in Artificial neural network tackles topics such as Topology which are related to areas like Complex network.

His work carried out in the field of Exponential stability brings together such families of science as Equilibrium point, Cellular neural network, Mathematical analysis and Bidirectional associative memory. His Stability study combines topics from a wide range of disciplines, such as Hopf bifurcation and Bifurcation. He works mostly in the field of Nonlinear system, limiting it down to topics relating to Multi-agent system and, in certain cases, Topology.

He most often published in these fields:

  • Control theory (74.76%)
  • Artificial neural network (51.14%)
  • Exponential stability (25.05%)

What were the highlights of his more recent work (between 2020-2021)?

  • Control theory (74.76%)
  • Artificial neural network (51.14%)
  • Control theory (12.92%)

In recent papers he was focusing on the following fields of study:

The scientist’s investigation covers issues in Control theory, Artificial neural network, Control theory, Topology and Applied mathematics. His Control theory research is multidisciplinary, incorporating elements of Multi-agent system, Markov process and Asynchronous communication. His studies deal with areas such as State, Order, Stability and Synchronization as well as Artificial neural network.

Within one scientific family, he focuses on topics pertaining to Interval under Synchronization, and may sometimes address concerns connected to Linear matrix inequality. His research in Topology intersects with topics in Correctness, Complex network and Bifurcation. His work deals with themes such as Matrix and Variable, which intersect with Applied mathematics.

Between 2020 and 2021, his most popular works were:

  • Stability and Stabilization in Probability of Probabilistic Boolean Networks (23 citations)
  • A Fuzzy Lyapunov Function Approach to Positive L l Observer Design for Positive Fuzzy Semi-Markovian Switching Systems With Its Application (22 citations)
  • Event-Based Secure Leader-Following Consensus Control for Multiagent Systems With Multiple Cyber Attacks (20 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Mathematical analysis
  • Statistics

His primary areas of investigation include Control theory, Nonlinear system, Artificial neural network, Multi-agent system and Topology. His Control theory research is multidisciplinary, relying on both Control, Markov chain and Asynchronous communication. Jinde Cao is interested in Exponential stability, which is a field of Nonlinear system.

Jinde Cao interconnects Extension, Mathematical optimization and Product in the investigation of issues within Exponential stability. His biological study spans a wide range of topics, including Correctness, Interval, Stability and Synchronization. His Topology research is multidisciplinary, incorporating perspectives in Memristor and Complex network.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Brief paper: A unified synchronization criterion for impulsive dynamical networks

Jianquan Lu;Daniel W. C. Ho;Jinde Cao.
Automatica (2010)

697 Citations

Global asymptotic stability of a general class of recurrent neural networks with time-varying delays

J. Cao;Jun Wang.
IEEE Transactions on Circuits and Systems I-regular Papers (2003)

598 Citations

Global asymptotic and robust stability of recurrent neural networks with time delays

Jinde Cao;Jun Wang.
IEEE Transactions on Circuits and Systems I-regular Papers (2005)

522 Citations

Brief paper: Second-order consensus in multi-agent dynamical systems with sampled position data

Wenwu Yu;Wei Xing Zheng;Guanrong Chen;Wei Ren.
Automatica (2011)

518 Citations

Second-order leader-following consensus of nonlinear multi-agent systems via pinning control

Qiang Song;Qiang Song;Jinde Cao;Wenwu Yu;Wenwu Yu.
Systems & Control Letters (2010)

494 Citations

Exponential stability and periodic oscillatory solution in BAM networks with delays

Jinde Cao;Lin Wang.
IEEE Transactions on Neural Networks (2002)

480 Citations

Boundedness and stability for Cohen–Grossberg neural network with time-varying delays☆

Jinde Cao;Jinling Liang.
Journal of Mathematical Analysis and Applications (2004)

380 Citations

Matrix measure strategies for stability and synchronization of inertial BAM neural network with time delays

Jinde Cao;Jinde Cao;Ying Wan.
Neural Networks (2014)

380 Citations

Adaptive synchronization of neural networks with or without time-varying delay.

Jinde Cao;Jianquan Lu.
Chaos (2006)

376 Citations

Stability analysis of delayed cellular neural networks

Jinde Cao;Dongming Zhou.
Neural Networks (1998)

375 Citations

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