Global h-Synchronization for High-Order Delayed Inertial Neural Networks via Direct SORS Strategy
Junlan Wang, Xin Wang, Xian Zhang, Song Zhu
Abstract
This work studies the issue of global h-synchronization about high-order delayed inertial neural networks via a second-order response system (SORS) approach. Note that the h-synchronization is a flexible definition which can generalize different special synchronization types by choosing different regulation function <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\hbar $ </tex-math></inline-formula> . By constructing a regulation function-dependent Lyapunov–Krasovskii functional (RFD–LKF), a novel delay-dependent global h-synchronization criterion is obtained. Furthermore, an adaptive control algorithm is designed to estimate control gains online, which is useful to guarantee global h-synchronization performance as well as to decrease the control cost. And finally, the superiority of the method is verified via three numerical examples.