Finite-time synchronization of fractional order neural networks via LMI approach

This study investigates the finite-time synchronization of complex-valued fractional-order memristive neural networks (CVFOMNNs) with time-varying delays. By separating the model into real and imaginary parts, we design a unified sliding-mode surface and construct a suitable sliding-mode controller to synchronize the drive system state trajectories to those of the response system. We prove that the novel super-twisting sliding mode controller forces the error system to reach a sliding surface in a finite time and decreases the chattering effect. Moreover, the LMI conditions are formulated to guarantee the finite-time synchronization of the CVFOMNNs with time-varying delays by using a less conservative Lyapunov-Krasovskii function. Finally, numerical simulations are presented to validate the effectiveness of the theoretical results.

Finite-time synchronization of fractional order neural networks via LMI approach

Monday, July 14 at 6:00pm

Madina Otkel

"Finite-time synchronization of fractional order neural networks via LMI approach"

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