Jiang Yuxin, Zhu Song, Shen Mouquan, Wen Shiping, Mu Chaoxu
School of Mathematics, China University of Mining and Technology, Xuzhou, 221116, China.
College of Electrical Engineering and Control Science, Nanjing Technology University, Nanjing, 211816, China.
Neural Netw. 2025 Oct;190:107672. doi: 10.1016/j.neunet.2025.107672. Epub 2025 Jun 7.
The finite-time synchronization (FTS) for memristor-based fuzzy neural networks with inertial term (MFINNs) is studied in this literature. In order to enhance the performance, efficiency and adaptability of the system to complex application scenarios, the memristor and inertial term are considered in the fuzzy neural network (FNNs). Different from the corresponding researches on exponential/asymptotic synchronization, the FTS of MFINNs is first investigated. This work directly analyze the second-order system via nonreduced-order approach, which can better reflect the second-order system because they do not lose any important kinetic information.Subsequently, fuzzy state-feedback and adaptive control schemes are constructed to guarantee the FTS of MFINNs. The algebraic conditions on the FTS of MFINNs are obtained by selecting a suitable Lyapunov-Krasovskii functional. At last, a numerical simulation is presented to substantiate the advantages of the proposed results. And some comparisons with the latest method are demonstrated.
本文研究了具有惯性项的忆阻器模糊神经网络（MFINNs）的有限时间同步（FTS）。为了提高系统在复杂应用场景下的性能、效率和适应性，在模糊神经网络（FNNs）中考虑了忆阻器和惯性项。与指数/渐近同步的相应研究不同，首次研究了MFINNs的FTS。这项工作通过非降阶方法直接分析二阶系统，因为它们不会丢失任何重要的动力学信息，所以能更好地反映二阶系统。随后，构建了模糊状态反馈和自适应控制方案，以保证MFINNs的FTS。通过选择合适的Lyapunov-Krasovskii泛函，得到了MFINNs的FTS的代数条件。最后，进行了数值模拟，以证实所提结果的优势，并与最新方法进行了一些比较。
