中立型神经网络的自适应有限时间随机同步

黄畅; 陈巧玉

doi:10.12299/jsues.23-0211

中立型神经网络的自适应有限时间随机同步

doi: 10.12299/jsues.23-0211

黄畅,
陈巧玉^,

上海工程技术大学电子电气工程学院, 上海 201620

基金项目: 国家自然科学基金(61673257)

详细信息

作者简介:
黄畅：黄　畅（1997 − ），男，硕士生，研究方向为神经网络动力学分析与控制。E-mail：1054052728@qq.com

通讯作者:
陈巧玉（1984 − ），女，副教授，博士，研究方向为复杂网络。E-mail：goodluckqiaoyu@126.com

中图分类号: O175.13
计量
- 文章访问数: 6
- HTML全文浏览量: 4
- PDF下载量: 1
- 被引次数: 0
出版历程
- 收稿日期: 2023-10-06
- 刊出日期: 2025-05-19

Adaptive finite-time stochastic synchronization of neutral-type neural networks

HUANG Chang,
CHEN Qiaoyu^,

School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China

摘要

摘要: 研究了一类中立型时滞神经网络的自适应有限时间随机同步问题。建立具有不确定和马尔可夫跳变参数的中立型时滞神经网络，通过自适应控制策略，得到主从系统的有限时间稳定性准则。根据伊藤公式和Lyapunov稳定性理论，获得中立型神经网络有限时间同步的充分条件，并估计其同步时间。数值例子验证了该方法的可行性。
- 中立型神经网络 /
- 自适应控制 /
- 马尔可夫跳变 /
- 有限时间同步 /
- 随机时滞神经网络
Abstract: The adaptive finite-time stochastic synchronization problem of a class of neutral-type neural networks was investigated. The neutral-type neural networks with uncertainty and Markovian jumping parameters was constructed. By employing an adaptive control strategy, the finite time stability criterion of the master-slave system was derived. Furthermore, based on the Itô formula and Lyapunov stability theory, sufficient conditions for finite-time stochastic synchronization of neutral-type neural networks were established, and their synchronization time was estimated. The feasibility of the proposed method was demonstrated through numerical examples.
- neutral-type neural networks /
- adaptive control /
- Markovian jumping /
- finite-time synchronization /
- stochastic delayed neural networks

HTML全文

图 1 马尔可夫跳变

Figure 1. Markovian jumping

下载: 全尺寸图片幻灯片

图 2 误差系统的状态

Figure 2. State of error system

下载: 全尺寸图片幻灯片

参考文献(18)

[1]	曹娟, 任凤丽. Markov切换时滞基因调控网络的均方同步和随机无源同步[J] . 应用数学和力学,2022,43(2):198 − 206.
[2]	廉玉波, 凌和平, 王钧斌, 等. 基于混合高斯-隐马尔可夫模型的动力电池实时热失控检测[J] . 汽车工程,2023,45(1):139 − 146.
[3]	舒服华, 杨慧芳. 基于灰色马尔科夫模型我国非织造布产量预测[J] . 上海工程技术大学学报,2017,31(3):257 − 261. doi: 10.3969/j.issn.1009-444X.2017.03.015
[4]	张全明, 崔晓昱, 张笑弟, 等. 计及用户不确定性的多时段耦合需求响应激励优化策略[J] . 中国电机工程学报,2022,42(24):8844 − 8854.
[5]	KOLMANOVSKII V B, NOSOV V R. Stability of functional differential equations[M] . Amsterdam: Elsevier Science Publishers, 1986.
[6]	NICULESCU S I. Delay effects on stability: a robust control approach[M] . London: Springer-Verlag, 2001.
[7]	GAO J, DAI L. Anti-periodic synchronization of Clifford-valued neutral-type recurrent neural networks with D operator[J] . IEEE Access,2022,10:9519 − 9528. doi: 10.1109/ACCESS.2022.3144486
[8]	ZHOU Z, ZHANG Z, CHEN M. Finite-time synchronization for fuzzy delayed neutral-type inertial BAM neural networks via the figure analysis approach[J] . International Journal of Fuzzy Systems,2022,24:229 − 246. doi: 10.1007/s40815-021-01132-8
[9]	CHEN H, SHI P, LIM C C. Cluster synchronization for neutral stochastic delay networks via intermittent adaptive control[J] . IEEE Transactions on Neural Networks and Learning Systems,2019,30(11):3246 − 3259. doi: 10.1109/TNNLS.2018.2890269
[10]	LI Y, ZHANG J, LU J, et al. Finite-time synchronization of complex networks with partial communication channels failure[J] . Information Sciences,2023,634:539 − 549. doi: 10.1016/j.ins.2023.03.077
[11]	WANG J L, WANG Q, WU H N, et al. Finite-time output synchronization and H∞ output synchronization of coupled neural networks with multiple output couplings[J] . IEEE Transactions on Cybernetics,2020,51(12):6041 − 6053.
[12]	LI Y X, HOU Z, CHE W W, et al. Event-based design of finite-time adaptive control of uncertain nonlinear systems[J] . IEEE Transactions on Neural Networks and Learning Systems,2021,33(8):3804 − 3813.
[13]	LIAN S, MENG W, LIN Z, et al. Adaptive attitude control of a quadrotor using fast nonsingular terminal sliding mode[J] . IEEE Transactions on Industrial Electronics,2021,69(2):1597 − 1607.
[14]	XU Y, LIU W, WU Y, et al. Finite-time synchronization of fractional-order fuzzy time-varying coupled neural networks subject to reaction-diffusion[J] . IEEE Transactions on Fuzzy Systems,2023,31(10):3423 − 3432. doi: 10.1109/TFUZZ.2023.3257100
[15]	HE J J, LIN Y Q, GE M F, et al. Adaptive finite-time cluster synchronization of neutral-type coupled neural networks with mixed delays[J] . Neurocomputing,2020,384:11 − 20. doi: 10.1016/j.neucom.2019.11.046
[16]	PAN L, CAO J. Stochastic quasi-synchronization for delayed dynamical networks via intermittent control[J] . Communications in Nonlinear Science and Numerical Simulation,2012,17(3):1332 − 1343. doi: 10.1016/j.cnsns.2011.07.010
[17]	CHEN W, JIAO L C. Finite-time stability theorem of stochastic nonlinear systems[J] . Automatica,2010,46(12):2105 − 2108. doi: 10.1016/j.automatica.2010.08.009
[18]	XU Y, WU X, WAN X, et al. Finite/fixed-time synchronization of multi-layer networks based on energy consumption estimation[J] . IEEE Transactions on Circuits and Systems I: Regular Papers,2021,68(10):4278 − 428. doi: 10.1109/TCSI.2021.3096211