School of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China.
School of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China.
ISA Trans. 2022 Nov;130:692-701. doi: 10.1016/j.isatra.2022.08.011. Epub 2022 Aug 20.
The finite-time synchronization issue of reaction-diffusion memristive neural networks (RDMNNs) is studied in this paper. To better synchronize the parameter-varying drive and response systems, an innovative gain-scheduled integral sliding mode control scheme is proposed, where the 2 controller gains can be scheduled and an integral switching surface function that contains a discontinuous term is involved. Moreover, by constructing a novel Lyapunov-Krasovskii functional and combining reciprocally convex combination (RCC) method, a less conservative finite-time synchronization criterion for RDMNNs is derived in the form of linear matrix inequalities (LMIs). Finally, three numerical simulations are exploited to illustrate the effectiveness, superiority and practicability of this paper.
本文研究了反应扩散忆阻神经网络（RDMNNs）的有限时间同步问题。为了更好地同步参数变化的驱动和响应系统，提出了一种创新的增益调度积分滑模控制方案，其中 2 个控制器增益可以调度，并且涉及包含不连续项的积分切换面函数。此外，通过构建一个新的李雅普诺夫-克拉索夫斯基泛函，并结合互凸组合（RCC）方法，以线性矩阵不等式（LMIs）的形式推导出 RDMNNs 的更保守的有限时间同步准则。最后，通过三个数值模拟来说明本文的有效性、优越性和实用性。
