| 具有混合延时和不同时标的混沌忆阻竞争神经网络的自适应同步 |
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| 引用本文： | 王有刚,武怀勤.具有混合延时和不同时标的混沌忆阻竞争神经网络的自适应同步[J].南京气象学院学报,2017,9(5):472-479. |
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| 作者姓名： | 王有刚 武怀勤 |
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| 作者单位： | 吕梁学院 数学系, 吕梁, 033001;燕山大学 理学院, 秦皇岛, 066004,燕山大学 理学院, 秦皇岛, 066004 |
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| 基金项目： | 国家自然科学基金（61573306）；河北省自然科学基金（A2011203103）；河北省高水平人才资助项目（C2015003054） |
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| 摘 要： | 本文考虑了具有混合时变延时和不同时标的混沌忆阻竞争神经网络的自适应同步问题.使用Lyapunov泛函方法和不等式分析技术，设计了一类新的具有反馈控制律的自适应控制器以取得网络同步及指数同步目的，提出了不用过多计算，如求解线性矩阵不等式或复杂代数计算的保证网络同步条件；同时，所获条件也可以应用到已有文献里关于忆阻器网络不同数学模型中.最后，通过实例验证了本文获得的理论结果的有效和正确性.
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| 关 键 词： | 自适应同步 忆阻器 竞争神经网络 时间延时 时标 |
| 收稿时间： | 2017/5/31 0:00:00 |
Adaptive synchronization of chaotic memristive competitive neural networks with mixed time-varying delays and different time scales |
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| WANG Yougang and WU Huaiqin.Adaptive synchronization of chaotic memristive competitive neural networks with mixed time-varying delays and different time scales[J].Journal of Nanjing Institute of Meteorology,2017,9(5):472-479. |
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| Authors: | WANG Yougang and WU Huaiqin |
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| Institution: | School of Mathematics, Lüliang University, Lüliang 033001;School of Science, Yanshan University, Qinhuangdao 066004 and School of Science, Yanshan University, Qinhuangdao 066004 |
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| Abstract: | This paper is concerned with the issue of adaptive synchronization for a general class of chaotic memristive competitive neural networks (MCNNs) with mixed time-varying delays and different time scales.By applying Lyapunov functional method and inequality analysis technique,a kind of novel adaptive controllers with feedback control laws are designed to achieve synchronization and exponential synchronization.The synchronization conditions,without need of excessive calculation such as solving linear matrix inequality or computing algebraic conditions,are given,which can be applied in MCNNs with different mathematical models of memristor proposed in the existing literature.Finally,an example with numerical simulations is listed to prove the efficiency and accuracy of the theoretical results. |
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| Keywords: | adaptive synchronization memristor competitive neural networks time delay time scale |
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