基于非降阶法的模糊惯性神经网络的固定时间同步

Fixed-time synchronization of fuzzy inertial neural networks based on non-reduced order approach

中文关键词  不连续激活函数  模糊惯性神经网络  固定时间同步  非降阶法

英文关键词  discontinuous activation  fuzzy inertial neural networks  fixed-time synchronization  non-reduced order approach

中文摘要

      截止目前,关于惯性神经网络的固定时间同步分析大都采用变量代换将其进行降阶处理,这种降阶法虽然 有效,但增加了系统维数,消除了惯性项对系统的影响.因此,基于非降阶的方法,本文研究了一类时滞模糊惯性神 经网络的固定时间同步控制问题.在Filippov解的定义和有限时间稳定的理论框架下,通过设计非线性反馈控制器, 获得了确保该类神经网络在固定时间内实现同步的一些同步准则.此外,通过应用一些不等式技巧,估算出了同步 时间的上界,可为其应用于实际工程中提供可靠性保证.最后,通过数值算例验证了本文所得结果的可靠性,并将 其应用于图像加解密.

英文摘要

      Up to now, the fixed-time synchronization analysis of inertial neural networks has mainly utilized variable substitution method, which is effective, but expands the system’s dimension and eliminates the influence of the inertial term. In order to consider the intuitive effects of the inertia term, this paper discusses the fixed-time synchronization of delayed fuzzy neural networks with inertia terms via using the non-reduced-order approach. Under the Filippov solution framework and finite time stability theory, some synchronization criteria are obtained to ensure the realization of fixed-time synchronization of the proposed neural system by designing nonlinear feedback controller. In addition, the upper bound of the synchronization time is estimated by using some inequality techniques, which can provide reliability guarantee for its application in practical engineering. Finally, the results obtained in this article are verified through numerical examples and an image encryption application.