Synchronization of BAM Cohen--Grossberg FCNNs with mixed time delays

IF 1.2 4区 数学 Q1 MATHEMATICS Iranian Journal of Fuzzy Systems Pub Date : 2021-04-01 DOI:10.22111/IJFS.2021.5921
M. Manikandan, K. Ratnavelu, P. Balasubramaniam, S. Ong
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引用次数: 2

Abstract

This paper deals with the synchronization problem of bidirectional associative memory (BAM) Cohen-Grossberg fuzzy cellular neural networks (CGFCNNs) with discrete time-varying and unbounded distributed delays. Some sufficient conditions are obtained to guarantee the robust synchronizationof BAM CGFCNNs with discrete time-varying and unbounded distributed delays subjected to parametric uncertainty by using Lyapunov-Krasovskii (LK) functional and Linear matrix inequality (LMI) approach.Sufficient criteria ensure that the error dynamics of considered system is globally asymptotically stable. Finally, numerical examples with simulations are given to show the efficacy of the derived results.
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混合时延下BAM Cohen- Grossberg fcnn的同步
研究了具有离散时变无界分布延迟的双向联想记忆(BAM) Cohen-Grossberg模糊细胞神经网络(CGFCNNs)的同步问题。利用Lyapunov-Krasovskii (LK)泛函和线性矩阵不等式(LMI)方法,得到了具有参数不确定性的离散时变无界分布延迟的BAM cgfcnn鲁棒同步的充分条件。充分的判据保证所考虑系统的误差动力学是全局渐近稳定的。最后,通过数值算例验证了所得结果的有效性。
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来源期刊
CiteScore
3.50
自引率
16.70%
发文量
0
期刊介绍: The two-monthly Iranian Journal of Fuzzy Systems (IJFS) aims to provide an international forum for refereed original research works in the theory and applications of fuzzy sets and systems in the areas of foundations, pure mathematics, artificial intelligence, control, robotics, data analysis, data mining, decision making, finance and management, information systems, operations research, pattern recognition and image processing, soft computing and uncertainty modeling. Manuscripts submitted to the IJFS must be original unpublished work and should not be in consideration for publication elsewhere.
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