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复杂系统与复杂性科学  2026, Vol. 23 Issue (2): 144-151    DOI: 10.13306/j.1672-3813.2026.02.018
  研究前沿 本期目录 | 过刊浏览 | 高级检索 |
具有反应扩散项的时滞忆阻神经网络系统H状态估计
于鹏飞, 林文娟
青岛大学 a.自动化学院; b.山东省工业控制技术重点实验室,山东 青岛 266071
H State Estimation of Delayed Memristive Neural Networks with Reaction-diffusion Terms
YU Pengfei, LIN Wenjuan
a. School of Automation; b. Shandong Key Laboratory of Industrial Control Technology, Qingdao University, Qingdao 266071, China
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摘要 为了更精确地获得具有反应扩散项的时滞忆阻神经网络的状态信息,设计了一个H状态估计器。首先,基于Lyapunov-Krasovskii(L-K)泛函方法,构造了新的时滞乘积型增广L-K泛函来处理时变时滞带来的影响。然后,通过利用自由权矩阵、Wirtinger积分不等式、改进逆凸矩阵不等式等方法进一步降低所得结果的保守性。同时,使用Dirichlet边界条件、Green公式等来处理系统中存在的反应扩散项;最终,给出误差系统全局渐近稳定且满足特定H性能指标的充分条件。在此基础上,以线性矩阵不等式的形式给出状态估计器的设计方法。最后,通过数值仿真验证所设计的H状态估计器的有效性。
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于鹏飞
林文娟
关键词 忆阻神经网络反应扩散项状态估计时滞Lyapunov-Krasovskii泛函    
Abstract:In order to obtain the state information of delayed memristive neural networks with reaction-diffusion terms more accurately, this paper designs a H state estimator. Firstly, based on the Lyapunov-Krasovskii (L-K) functional method, a new delay-product-type augmented L-K functional is constructed to handle the effects of time-varying delays. Then, by utilizing techniques such as free-weight-matrix method, Wirtinger-based integral inequality, and extended reciprocally convex matrix inequality, further reduction in conservatism of the obtained results is achieved. Meanwhile, Dirichlet boundary conditions and Green formula, among others, are employed to address the reaction-diffusion terms of the system; finally, sufficient conditions for the global asymptotic stability of the error system that meet specific H performance criteria are provided. Building upon this foundation, we present a design methodology for state estimator in terms of linear matrix inequalities. Finally, a numerical example is given to verify the effectiveness of the proposedHstate estimator.
Key wordsmemristive neural networks    reaction-diffusion terms    state estimation    time-delay    Lyapunov-Krasovskii functional
收稿日期: 2024-03-29      出版日期: 2026-05-19
:  TP183  
基金资助:国家自然科学基金(62103213),山东省自然科学基金青年基金(ZR202102190089),山东省优秀青年科学基金(海外)(2023HWYQ-086),中国博士后科学基金(2022M721748)
通讯作者: 林文娟(1993-),女,山东青岛人,博士,教授,主要研究方向为时滞系统鲁棒控制、网络化系统分析与设计。   
作者简介: 于鹏飞(2000-),男,山东青岛人,硕士研究生,主要研究方向为忆阻神经网络系统状态估计。
引用本文:   
于鹏飞, 林文娟. 具有反应扩散项的时滞忆阻神经网络系统H状态估计[J]. 复杂系统与复杂性科学, 2026, 23(2): 144-151.
YU Pengfei, LIN Wenjuan. H State Estimation of Delayed Memristive Neural Networks with Reaction-diffusion Terms[J]. Complex Systems and Complexity Science, 2026, 23(2): 144-151.
链接本文:  
https://fzkx.qdu.edu.cn/CN/10.13306/j.1672-3813.2026.02.018      或      https://fzkx.qdu.edu.cn/CN/Y2026/V23/I2/144
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