Finite-time Synchronization of Delayed Inertial Neural Networks via Intermittent Control
Yuxin Jiang, Song Chun Zhu · 2024
This paper introduces the inertial neural network model with time-delays at first. Through reduced-order approach, the second-order neural networks can be transformed into two first-order differential equations, which simplify the models. Then, by designing the periodically intermittent control strategy, some algebraic conditions for the finite-time synchronization of delayed inertial neural networks are derived by virtue of finite time stability theorems and inequality techniques. What's more, the constructed controller here is independent with delays. Compared with delay-dependent controller, the controller herein is more suitable for practical applications. Moreover, the settling time of delayed inertial neural networks is estimated. In addition, a numerical example is given to verify the effectiveness of the criteria.