S-分布时滞静态神经网络的全局指数收敛性
Global Exponential Convergence of Static Neural Networks with S-Type Distributed Delays
摘要: 本文研究了一类具有S-分布时滞的静态神经网络，主要利用微分不等式技巧，建立了所讨论模型的解指数收敛到零的充分性条件，并给出了实例说明了所得结论的有效性。
Abstract: This paper is concerned with the exponential convergence for a class of static neural networks with S-type distributed delays. By applying the differential inequality techniques, the sufficient conditions to ensure that all solutions of the addressed system converge exponentially to zero are established. Moreover, an example is given to show the effectiveness of the obtained results.
文章引用：张若军, 张静静. S-分布时滞静态神经网络的全局指数收敛性[J]. 应用数学进展, 2018, 7(9): 1191-1196. https://doi.org/10.12677/AAM.2018.79138

参考文献

[1] 马天瑾. 神经网络技术[M]. 青岛: 青岛海洋出版社, 1994.
[2] 阎平凡. 人工神经网络与模拟进化计算[M]. 北京: 清华大学出版社, 2001.
[3] 阮炯. 神经动力学模型方法与应用[M]. 北京: 科学出版社, 2001.
[4] Qiao, H., et al. (2003) A Reference Model Approach to Stability Analysis of Neural Networks. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernet-ics), 33, 925-936. [Google Scholar] [CrossRef] 
[5] 王林山. 时滞递归神经网络[M]. 北京: 科学出版社, 2007.
[6] Wang, L.S. and Xu, D.Y. (2002) Global Asymptotic Stability of Bidirectional Associative Memory Neural Networks with S-Type Distributed Delays. International Journal of Systems Science, 33, 869-877. [Google Scholar] [CrossRef] 
[7] Kwon, O.M., et al. (2014) New and Improved Results on Stability of Static Neural Networks with Interval Time-Varying Delays. Applied Mathematics and Computation, 239, 346-357. [Google Scholar] [CrossRef] 
[8] Liu, B., Ma, X.L. and Jia, X.-C. (2018) Further Results on H∞ State Estimation of Static Neural Networks with Time-Varying Delay. Neurocomputing, 285, 133-140. [Google Scholar] [CrossRef] 
[9] Manivannan, R., Samidurai, R. and Zhu, Q.X. (2017) Further Improved Results on Stability and Dissipativity Analysis of Static Impulsive Neural Networks with Interval Time-Varying Delays. Journal of the Franklin Institute, 354, 6312-6340. [Google Scholar] [CrossRef] 
[10] Arbi, A., et al. (2015) Stability Analysis for Delayed High-Order Type of Hopfield Neural Networks with Impulses. Neurocomputing, 165, 312-329. [Google Scholar] [CrossRef] 
[11] Xu, C.J. and Li, P.L. (2017) Global Exponential Convergence of Neu-tral-Type Hopfield Neural Networks with Multi-Proportional Delays and Leakage Delays. Chaos, Solitons & Fractals, 96, 139-144. [Google Scholar] [CrossRef] 
[12] Wan, L., Zhou, Q.H. and Liu, J. (2017) Delay-Dependent Attractor Analysis of Hopfield Neural Networks with Time-Varying Delays. Chaos, Solitons & Fractals, 101, 68-72. [Google Scholar] [CrossRef] 

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