Abstract
This article is devoted to the cluster synchronization issue of coupled fractional-order neural networks. By introducing the stability theory of fractional-order differential systems and the framework of Filippov regularization, some sufficient conditions are derived for ascertaining the asymptotic and finite-Time cluster synchronization of coupled fractional-order neural networks, respectively. In addition, the upper bound of the settling time for finite-Time cluster synchronization is estimated. Compared with the existing works, the results herein are applicable for fractional-order systems, which could be regarded as an extension of integer-order ones. A numerical example with different cases is presented to illustrate the validity of theoretical results.
| Original language | English |
|---|---|
| Article number | 8961184 |
| Pages (from-to) | 4956-4967 |
| Journal | IEEE Transactions on Neural Networks and Learning Systems |
| Volume | 31 |
| Issue number | 11 |
| Online published | 16 Jan 2020 |
| DOIs | |
| Publication status | Published - Nov 2020 |
Research Keywords
- Filippov solution
- finite-Time cluster synchronization
- fractional-order neural networks
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Dive into the research topics of 'Asymptotic and Finite-Time Cluster Synchronization of Coupled Fractional-Order Neural Networks with Time Delay'. Together they form a unique fingerprint.Projects
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GRF: Intelligent Mission Planning and Tracking Control of Autonomous Surface Vehicles Based on Neural Computation
WANG, J. (Principal Investigator / Project Coordinator)
1/01/19 → 3/01/24
Project: Research
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GRF: Analysis and Design of Multiscale Neurodynamic Systems with Their Applications for Robust Control, Data Processing, and Supervised Learning
WANG, J. (Principal Investigator / Project Coordinator)
1/01/18 → 20/12/22
Project: Research
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