Research Center for Complex Networks & Swarm Intelligence, School of Computer Science & Technology, Jiangsu Normal University, Xuzhou 221116, China.
Research Center for Complex Networks & Swarm Intelligence, School of Computer Science & Technology, Jiangsu Normal University, Xuzhou 221116, China.
Neural Netw. 2021 Jan;133:32-39. doi: 10.1016/j.neunet.2020.10.007. Epub 2020 Oct 16.
This paper considers the prespecified-time synchronization issue of switched coupled neural networks (SCNNs) under some smooth controllers. Different from the traditional finite-time synchronization (FTS), the synchronization time obtained in this paper is independent of control gains, initial values or network topology, which can be pre-set as to the task requirements. Moreover, unlike the existing nonsmooth or even discontinuous FTS control strategies, the new proposed control protocols are fully smooth, which abandon the common fractional power feedbacks or signum functions. Finally, two illustrative examples are provided to illustrate the effectiveness of the theoretical results.
本文考虑了在一些平滑控制器下切换耦合神经网络(SCNNs)的预设时间同步问题。与传统的有限时间同步(FTS)不同,本文得到的同步时间与控制增益、初始值或网络拓扑无关,可以根据任务要求进行预设。此外,与现有的非平滑甚至不连续的 FTS 控制策略不同,新提出的控制协议是完全平滑的,摒弃了常见的分数幂反馈或符号函数。最后,提供了两个说明性的例子来说明理论结果的有效性。