Stability and synchronization of fractional-order generalized reaction–diffusion neural networks with multiple time delays and parameter mismatch

Stability and synchronization of fractional-order generalized reaction–diffusion neural networks with multiple time delays and parameter mismatch

In this paper, stability and synchronization of fractional-order generalized reaction–diffusion neural networks with multiple time delays and parameter mismatch are investigated. The global uniform stability conditions of fractional-order generalized reaction–diffusion neural networks are derived. F...

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Veröffentlicht in:Neural computing & applications 2022-10, Vol.34 (20), p.17905-17920
Hauptverfasser: Gu, Yajuan, Wang, Hu, Yu, Yongguang
Format: Artikel
Sprache:eng
Schlagworte:
Artificial Intelligence
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Control theory
Data Mining and Knowledge Discovery
Diffusion
Fault diagnosis
Image Processing and Computer Vision
Investigations
Liapunov direct method
Mathematical functions
Neural networks
Original Article
Parameters
Probability and Statistics in Computer Science
Stability
Synchronism
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Zusammenfassung:In this paper, stability and synchronization of fractional-order generalized reaction–diffusion neural networks with multiple time delays and parameter mismatch are investigated. The global uniform stability conditions of fractional-order generalized reaction–diffusion neural networks are derived. Furthermore, considering parameter mismatch, the global synchronization conditions of fractional-order generalized reaction–diffusion neural networks with multiple time delays are given via the Lyapunov direct method. Finally, two numerical examples are presented to show the effectiveness of our theoretical results.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-022-07414-y