Finite time dynamic analysis of memristor-based fuzzy NNs with inertial term: Nonreduced-order approach

IF 6 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Neural Networks Pub Date : 2025-06-07 DOI:10.1016/j.neunet.2025.107672

Yuxin Jiang , Song Zhu , Mouquan Shen , Shiping Wen , Chaoxu Mu

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引用次数: 0

Abstract

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.

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具有惯性项的记忆电阻模糊神经网络的有限时间动态分析：非降阶方法

本文研究了基于忆阻器的含惯性项模糊神经网络的有限时间同步问题。为了提高系统的性能、效率和对复杂应用场景的适应性，模糊神经网络（FNNs）中考虑了记忆电阻和惯性项。与指数/渐近同步的相关研究不同，本文首先研究了mfinn的傅立叶变换。本文采用非降阶方法直接分析了二阶系统，由于二阶系统不丢失任何重要的动力学信息，可以更好地反映二阶系统。在此基础上，构造了模糊状态反馈和自适应控制方案，以保证mfinn的傅立叶变换。通过选取合适的Lyapunov-Krasovskii泛函，得到了mfinn的傅里叶变换的代数条件。最后，通过数值模拟验证了所提结果的优越性。并与最新方法进行了比较。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Neural Networks 工程技术-计算机：人工智能

CiteScore

13.90

自引率

7.70%

发文量

425

审稿时长

67 days

期刊介绍： Neural Networks is a platform that aims to foster an international community of scholars and practitioners interested in neural networks, deep learning, and other approaches to artificial intelligence and machine learning. Our journal invites submissions covering various aspects of neural networks research, from computational neuroscience and cognitive modeling to mathematical analyses and engineering applications. By providing a forum for interdisciplinary discussions between biology and technology, we aim to encourage the development of biologically-inspired artificial intelligence.