Extended Dissipativity and Non-Fragile Synchronization for Recurrent Neural Networks With Multiple Time-Varying Delays via Sampled-Data Control

Extended Dissipativity and Non-Fragile Synchronization for Recurrent Neural Networks With Multiple Time-Varying Delays via Sampled-Data Control

This paper deals with the extended dissipativity and non-fragile synchronization of delayed recurrent neural networks (RNNs) with multiple time-varying delays and sampled-data control. A suitable Lyapunov-Krasovskii Functional (LKF) is built up to prove the quadratically stable and extended dissipat...

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Bibliographic Details
Main Authors: R. Anbuvithya, S. Dheepika Sri, R. Vadivel, Nallappan Gunasekaran, Porpattama Hammachukiattikul
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Dissipative analysis
multiple time-varying delay
recurrent neural networks
synchronization
sampled-data control
Online Access:https://ieeexplore.ieee.org/document/9357345/

Internet

https://ieeexplore.ieee.org/document/9357345/

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