Passivity for Multiadaptive Coupled Fractional- Order Reaction-Diffusion Neural Networks-Reference-Cited by-同舟云学术

Passivity for Multiadaptive Coupled Fractional- Order Reaction-Diffusion Neural Networks

Published:2024-04 Issue:2 Volume:8 Page:1350-1361
ISSN:2471-285X
Container-title:IEEE Transactions on Emerging Topics in Computational Intelligence
language:
Short-container-title:IEEE Trans. Emerg. Top. Comput. Intell.

Author:

Wang Jin-Liang¹^ORCID,Liu Chen-Guang²^ORCID,Liu Xiao-Lu³^ORCID,Huang Lina⁴^ORCID,Huang Tingwen⁵^ORCID

Affiliation:

1. Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems, School of Computer Science and Technology, Tiangong University, Tianjin, China

2. Institute of Artificial Intelligence, Beihang University, Beijing, China

3. School of Computer Science and Technology, Tiangong University, Tianjin, China

4. Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University, Shanghai, China

5. Science Program, Texas A & M University at Qatar, Doha, Qatar

Funder

National Natural Science Foundation of China

Tianjin Natural Science Foundation for Distinguished Young Scholars

2021 Industry-University-Research Innovation Fund for Chinese Universities-Unmanned Cluster Collaborative Intelligence Project

Tianjin Research Innovation Project for Postgraduate Students, China

Tianjin Talent Development Special Support Program for Young Top-Notch Talent, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/7433297/10480102/10381644.pdf?arnumber=10381644

Reference53 articles.

1. Secure Communication Based on Quantized Synchronization of Chaotic Neural Networks Under an Event-Triggered Strategy

2. Stability of stochastic impulsive reaction–diffusion neural networks with S-type distributed delays and its application to image encryption

3. Improved Stability Criteria for Delayed Neural Networks Using a Quadratic Function Negative-Definiteness Approach

4. Intermittent Event-triggered Exponential Stabilization for State-Dependent Switched Fuzzy Neural Networks With Mixed Delays

5. Periodically Intermittent Stabilization of Neural Networks Based on Discrete-Time Observations

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