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Using an appropriate Lyapunov-Krasovskii functional, some delay-dependent sufficient conditions and an adaption law which include the master-slave parameters are established for designing a delayed synchronization law in terms of linear matrix inequalities.
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As a consequence, several families of control laws are designed to achieve globally exponential hyperchaos (time-delayed) synchronization, and globally exponential hyperchaos (without time delay) synchronization.
Interprocessor communication (PC) overheads have emerged as the major performance limitation in parallel processing systems, due to the transmission delays, synchronization overheads, and conflicts for shared communication resources created by data exchange.
The circuit delays the synchronization signal in sub-nanosecond; hence, we can recover high-resolution PSF by a deconvolution approach.
We have studied delay-induced synchronization transitions on attractively and repulsively coupled scale-free neuronal networks that were locally modeled by the Rulkov map.
Particular attention is given to globally exponential hyperchaos (time-delayed) synchronization and control for this hyperchaotic system.
A new synchronization strategy, namely a mixed fuzzy delayed feedback dissipativity-based synchronization strategy, is proposed for chaotic systems with external disturbance.
This paper is devoted to the global exponential and fixed-time synchronization of delayed reaction-diffusion Cohen Grossberg neural networks.
This paper investigates the exponential synchronization of delayed memristor-based neural networks (MNNs) with discontinuous activation functions.
It is found that these regions are inherently bounded which restricts the graphs that allow for synchronization under delayed communication.
This paper addresses the controller design problem for global fixed-time synchronization of delayed neural networks (DNNs) with discontinuous activations.
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