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Non-fragile l2 l1 synchronization for switched inertial neural networks with random gain fluctuations: A persistent dwell-time switching law
هماهنگ سازی L2 L1 غیر شکننده برای شبکه های عصبی اینرسی غیرفعال با نوسانات افزایش تصادفی: قانون تعویض زمان ثابت-2020 This paper investigates the synchronization control issue for a set of switched inertial neural networks in
the discrete-time domain, in which the persistent dwell-time switching law is employed to depict the
switchings among system parameters. Thereinto, the foregoing networks having the second-order differential
equations are degraded to the first-order differential ones through applying the variable transformation.
In addition, in order to cope with random gain fluctuations caused by noise or harsh
environments, in the controller, two random variables obeying the Bernoulli distribution are employed
to simulate the occurrence of gain fluctuations. Based on Lyapunov stability theory, persistent dwelltime
concept and stochastic analysis theory, some sufficient criteria are derived under which the synchronization
error system is exponentially mean-square stable with a prescribed l2 l1 property.
Finally, a numerical example, including some illustrative simulations, is given to present the feasibility
of the derived analytical results. Keywords: Switched inertial neural networks |l2 l1 synchronization | Persistent dwell-time switching law | Randomly occurring gain fluctuations |
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