Event-Triggered Controller on Practically Exponential Input-to-State Stabilization of Stochastic Reaction–Diffusion Cohen–Grossberg Neural Networks and Its Application to Image Encryption

The stabilization problem for a class of stochastic reaction–diffusion delayed Cohen–Grossberg neural networks (SRDDCGNNs) with event-triggered controllers is addressed in this paper. Neumann boundary conditions, distributed and bounded external disturbances are introduced to solve such a problem. N...

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Bibliographic Details
Published in:Neural processing letters 2023-12, Vol.55 (8), p.11147-11171
Main Authors: Gokulakrishnan, V., Srinivasan, R.
Format: Article
Language:English
Subjects:
Artificial Intelligence
Boundary conditions
Brownian motion
Complex Systems
Computational Intelligence
Computer Science
Controllers
Criteria
Dengue fever
Design
Eigenvalues
Euclidean space
Event triggered control
Linear matrix inequalities
Mathematical analysis
Neural networks
Ordinary differential equations
Partial differential equations
Stabilization
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Summary:The stabilization problem for a class of stochastic reaction–diffusion delayed Cohen–Grossberg neural networks (SRDDCGNNs) with event-triggered controllers is addressed in this paper. Neumann boundary conditions, distributed and bounded external disturbances are introduced to solve such a problem. New sufficient criteria are derived using the 2-norm event generator and Lyapunov functional to ensure that the proposed controlled systems achieve practically exponential input-to-state stabilization in terms of the linear matrix inequality. Considering these criteria, the impact of an event-triggered controller on the practically exponential input-to-state stability is investigated. The Zeno phenomenon of the event-triggered controller is avoided. Moreover, the obtained results are successfully applied to SRDDCNNs and stochastic reaction–diffusion delayed Hopfield neural networks (SRDDHNNs). Finally, the main results are illustrated with simulation results and the SRDDHNNs are applied to image encryption.
ISSN:1370-4621
1573-773X
DOI:10.1007/s11063-023-11369-z