带有Hebbian学习型和比例延迟的二阶网络的有限时间稳定性 陈娟, 黄振坤集美大学理学院, 厦门 361021 Finite-time Stability of Second-order Networks with Hebbian-type Learning and Proportional Delays CHEN Juan, HUANG ZhenkunSchool of Siences, Jimei University, Xiamen 361021

摘要 图/表 参考文献 相关文章(5) 点击分布统计 下载分布统计 --> 全文: PDF(400 KB) HTML (1 KB) 输出: BibTeX | EndNote (RIS) 摘要本文研究了一类带有Hebbian学习型和比例延迟的二阶网络有限时间稳定性问题.通过微分不等式方法得到了一个全新的结果来保证系统的有限时间稳定性，同时建立了广义指数同步准则.所给出全新的充分条件推广并补充了已有文献的结果，且可应用于大多数的神经网络系统.最后，给出的例子验证所得结果的有效性和可行性. 服务 加入引用管理器 E-mail Alert RSS 收稿日期: 2017-09-08 PACS: O193 基金资助:国家自然科学基金（61573005），福建省自然科学基金（2018J01417，2019J01330），福建省教育厅（JT180264）资助项目. 引用本文: 陈娟, 黄振坤. 带有Hebbian学习型和比例延迟的二阶网络的有限时间稳定性[J]. 应用数学学报, 2019, 42(5): 614-628. CHEN Juan, HUANG Zhenkun. Finite-time Stability of Second-order Networks with Hebbian-type Learning and Proportional Delays. Acta Mathematicae Applicatae Sinica, 2019, 42(5): 614-628. 链接本文: http://123.57.41.99/jweb_yysxxb/CN/或 http://123.57.41.99/jweb_yysxxb/CN/Y2019/V42/I5/614 [1] Moulay E, Dambrine M, Yeganefar N, Perruquetti W. Finite-time stability and stabilization of time-delay systems. Systems Control Letters, 2008, 57(7):561-566 [2] Yang R, Wang Y. Finite-time stability and stabilization of a class of nonlinear time-delay systems. SIAM J. Control Optim., 2012, 50(5):3113-3131 [3] Yang R, Wang Y. Finite-time stability analysis and H control for a class of nonlinear time-delay Hamiltonian systems. Automatica, 2013, 49:390-401 [4] Hien L V. An explicit criterion for finite-time stability of linear nonautonomous systemswith delays. Applied Mathematics Letters, 2014, 30(4):12-18 [5] Amato F, Ariola M, Cosentino C. Finite-time control of discrete-time linear systems:Analysis and design conditions. Automatica, 2010, 46(5):919-924 [6] Garcia G, Tarbouriech S, Bernussou J. Finite-time stabilization of linear time-varying continuous systems. IEEE Transactions on Automatic Control, 2009, 54(2):364-369 [7] Xiao Q, Zeng Z G. Lagrange stability for T-S fuzzy memristive neural networks with time-varying delays on time scales. IEEE Transactions on Fuzzy Systems, 2018, 26(3):1091-1103 [8] Xiao Q, Huang Z K, Zeng Z G. Passivity analysis of memristor-based inertial neural networks with discrete and distributed delays. IEEE Transactions on Systems Man and Cybernetics:Systems, 2019, 49(2):375-385 [9] Song X L, Zhao P, Xu Z W, Peng J. Global asymptotic stability of CNNs with impulses and multiproportional delays. Mathematical Methods in the Applied Sciences, 2016, 39(4):722-733 [10] Huang Z K, Bin H H, Cao J D, Wang B Y. Synchronizing neural networks with proportional delays based on a class of q-type allowable time scales. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(8):3418-3428 [11] Huang Z K, Cao J D, Li J M, Bin H H. Quasi-synchronization of neural networks with parameter mismatches and delayed impulsive controller on time scales. Nonlinear Analysis:Hybrid Systems, 2019, 33:104-115 [12] Kamp Y, Hasler M. Recursive neural networks for associative memory. New York:Wiley, 1990 [13] Kosmatopoulos E B, Christodoulou M A. Structural properties of gradient recurrent high order neural networks. IEEE Transactions on Circuits and Systems I!I:Analog and Digital, Signal Processing, 1995, 42(9):592-603 [14] Kosmatopoulos E B, Polycarpou M M, Christodoulou M A, Ioannou P A. High order neural network structures for identification of dynamical systems. IEEE Transactions on Neural Networks, 1995, 6(2):422-431 [15] Liu X, Teo K L, Xu B. Exponential stability of impulsive high-order Hopfield type neural networks with time varying delays. IEEE Transactions on Neural Networks, 2005, 16(6):1329-1339 [16] Pei J, Xu D, Yang Z, Zhu W. Stability analysis of second order neural networks with time delays. Lecture Notes in Computer Science, Vol. 3496, Springer, Berlin, 2005, 241-246 [17] Xu B, Liu X, Liao X. Global exponential stability of high order Hopfield type neural networks. Applied Mathematics Computation, 2006, 174(1):98-116 [18] Gopalsamy K. Learning dynamics and stability in networks with fuzzy synapses. Dynamic Systems and Applications, 2006, 15:657-671 [19] Gopalsamy K. Learning dynamics in second order networks. Nonlinear Analysis:RealWorld Applications, 2007, 8(2):688-698 [20] Huang Z K, Feng C H, Sannay M, Ye J L. Multistable learning dynamics in second-order neural networks with time-varying delays. International Journal of Computer Mathematics, 2011, 88(7):1327-1346 [21] Song Q, Yang X, Li C. Stability analysis of nonlinear fractional-order systems with variable-time impulses. Journal of the Franklin Institute, 2017, 354(7):2959-2978 [22] Pecora L M, Carroll T L. Synchronization in chaotic system. Phys. Rev. Lett., 1990, 64(8):821-824 [23] Cheng S, Ji J C, Zhou J. Fast synchronization of directionally coupled chaotic systems. Applied Mathematical Modelling, 2013, 37(1-2):127-136 [24] Ye Z Y, Deng C B. Adaptive synchronization to a general non-autonomous chaotic system and its applications. Nonlinear Analysis:Real World Application, 2012, 13(2):840-849 [25] Hien L V, Doan T S. Finite-time stability of a class of non-autonomous neural networks with heterogeneous proportional delays. Applied Mathematics and Computation, 2015, 251:14-23 [26] Liu B W. Finite-time stability of a class of CNNs with heterogeneous proportional delays and oscillating leakage coefficients. Neural Processing Letters, 2017, 45(1):109-119 [27] Kosko B. Neural networks and fuzzy systems. New Delhi:Prentice-Hall, 1992 [28] Amari S I. Competitive and cooperative aspects in dynamics of neural excitation and self-organization. Lecture Notes in Biomathematics, Berlin:Springer, 1982, 45:1-28 [29] Amari S I. Mathematical theory of neural learning. New Generation Computing, 1991, 8(4):281-294 [30] Meyer-Baese A, Ohl F, Scheich H. Singular perturbation analysis of competitive neural networks with different time scales. Neural Comput., 1996, 8:545-563 [31] Meyer-Baese A, Pilyugin S S, Chen Y. Global exponential stability of competitive neural networks with different time scales. IEEE Trans. Neural Networks, 2003, 14:716-719 [32] Lu H, He Z. Global exponential stability of delayed competitive neural networks with different time scales. Neural Networks, 2005, 18:243-250 [33] Song Q, Yu Q, Zhao Z. Dynamics of complex-valued neural networks with variable coefficients and proportional delays. Neurocomputing, 2018, 275:2762-2768 [34] Song Q, Shu H, Zhao Z. Lagrange stability analysis for complex-valued neural networks with leakage delay and mixed time-varying delays. Neurocomputing, 2017, 244:33-41 [1] 孟益民, 黄立宏, 郭上江. 具分布时滞双向联想记忆神经网络周期解的存在性及全局稳定性[J]. 应用数学学报, 2018, 41(3): 369-387. [2] 周辉, 王文, 周宗福. 具非线性收获项和S-型时滞Lasota-Wazewska模型的概周期解[J]. 应用数学学报, 2017, 40(3): 471-480. [3] 宋海涛, 刘胜强. 具有一般复发现象的疾病模型的全局稳定性[J]. 应用数学学报, 2017, 40(1): 37-48. [4] 周辉, 周宗福. 具S-型分布时滞的细胞神经网络的概周期解[J]. 应用数学学报(英文版), 2013, 36(3): 521-531. [5] 赵文强, 李扬荣. 随机耗散Camassa-Holm方程的吸引子[J]. 应用数学学报(英文版), 2012, (1): 73-87. PDF全文下载地址: http://123.57.41.99/jweb_yysxxb/CN/article/downloadArticleFile.do?attachType=PDF&id=14654 相关话题/应用数学 比例 网络 统计 系统 领限时大额优惠券,享本站正版考研考试资料! 优惠券领取后72小时内有效，10万种最新考研考试考证类电子打印资料任你选。涵盖全国500余所院校考研专业课、200多种职业资格考试、1100多种经典教材，产品类型包含电子书、题库、全套资料以及视频，无论您是考研复习、考证刷题，还是考前冲刺等，不同类型的产品可满足您学习上的不同需求。 ... 考试优惠券 本站小编 Free壹佰分学习网 2022-09-19 任意初态下非线性不确定系统的迭代学习控制 任意初态下非线性不确定系统的迭代学习控制李国军1,2,陈东杰2,韩一士2,许中石21.浙江工业大学信息工程学院,杭州310023;2.浙江警察学院公共基础部,杭州310053IterativeLearningControlwithArbitraryInitialStatesforNonlinearS ... 中科院数学与系统科学研究院 本站小编 Free考研考试 2021-12-27 二阶奇异差分系统的正周期解 二阶奇异差分系统的正周期解许丽1,崔德标21.江苏省宿迁中学,宿迁223800;2.江苏省宿迁市宿城区水利局,宿迁223800PositivePeriodicSolutionsofSecondOrderSingularDifferenceSystemsXULi1,CUIDebiao21.Suqian ... 中科院数学与系统科学研究院 本站小编 Free考研考试 2021-12-27 病例-队列设计下长度偏差数据的比例均值剩余寿命模型的统计推断 病例-队列设计下长度偏差数据的比例均值剩余寿命模型的统计推断徐达1,周勇2,31.上海财经大学统计与管理学院,上海200082;2.华东师范大学经管学部交叉科学研究院及统计学院,上海200241;3.中国科学院数学与系统科学研究院,北京100190ProportionalMeanResidualLi ... 中科院数学与系统科学研究院 本站小编 Free考研考试 2021-12-27 非线性隐式分数阶微分方程耦合系统初值问题 非线性隐式分数阶微分方程耦合系统初值问题董佳华1,冯育强2,蒋君11.武汉科技大学理学院,武汉430065;2.冶金工业过程系统科学湖北省重点实验室,武汉430081TheProximalPointIterativeAlgorithmfortheInitialValueProblemforaCoup ... 中科院数学与系统科学研究院 本站小编 Free考研考试 2021-12-27 比例Volterra积分方程的切比雪夫谱配置法 比例Volterra积分方程的切比雪夫谱配置法郑伟珊韩山师范学院数学与统计学院,潮州521041ChebyshevSpectral-collocationMethodforProportionalVolterraIntegralEquationZHENGWeishanCollegeofMathema ... 中科院数学与系统科学研究院 本站小编 Free考研考试 2021-12-27 带有两个小世界联接的时滞环形神经网络系统的稳定性分析 带有两个小世界联接的时滞环形神经网络系统的稳定性分析李敏,赵东霞,毛莉中北大学理学院数学学科部,太原030051StabilityAnalysisofaDelayedRingNeuralNetworkwithTwoSmallWorldConnectionsLIMin,ZHAODongxia,MAOL ... 中科院数学与系统科学研究院 本站小编 Free考研考试 2021-12-27 离散切换正时滞系统在异步切换下的镇定性 离散切换正时滞系统在异步切换下的镇定性刘婷婷1,吴保卫2,刘丽丽2,王月娥21.西安工程大学理学院,西安710048;2.陕西师范大学数学与信息科学学院,西安710119StabilizationofDiscreteSwitchedPositiveTime-delaySystemsUnderAsyn ... 中科院数学与系统科学研究院 本站小编 Free考研考试 2021-12-27 基于分数阶滑模控制器的不确定分数阶混沌系统同步 基于分数阶滑模控制器的不确定分数阶混沌系统同步阎晓妹1,尚婷1,赵小国21.西安理工大学自动化与信息工程学院,西安710048;2.西安建筑科技大学机电工程学院,西安710055SynchronizationofUncertainFractional-orderChaoticSystemsBased ... 中科院数学与系统科学研究院 本站小编 Free考研考试 2021-12-27 H增生映射和含有广义(p,q)-Laplacian算子的非线性椭圆系统 H增生映射和含有广义(p,q)-Laplacian算子的非线性椭圆系统魏利1,张瑞兰1,RaviP.Agarwal2,31.河北经贸大学数学与统计学学院,石家庄050061;2.DepartmentofMathematics,TexasA&MUniversity-Kingsville,Kingsvi ... 中科院数学与系统科学研究院 本站小编 Free考研考试 2021-12-27 具分布时滞双向联想记忆神经网络周期解的存在性及全局稳定性 具分布时滞双向联想记忆神经网络周期解的存在性及全局稳定性孟益民1,黄立宏2,郭上江31.湖南大学数学与计量经济学院,长沙438000;2.长沙理工大学数学与统计学院,长沙430079;3.湖南大学数学与计量经济学院,长沙100190GlobalExistenceandStabilityofPerio ... 中科院数学与系统科学研究院 本站小编 Free考研考试 2021-12-27 Free考研考试FreeKaoYan.Com 欢迎来到Free考研考试，"为实现人生的Free而奋斗" 关于我们 联系我们 电子邮件 苏ICP备16059069号 © 2020 FreeKaoYan! . All rights reserved.