Exponential synchronization of general chaotic delayed neural networks via hybrid feedback
Exponential synchronization of general chaotic delayed neural networks via hybrid feedback
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通过混合反馈的一般混沌延迟神经网络的指数同步
DOI：
10.1631/jzus.a071336
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发表时间：
2008-01
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影响因子：
--
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中科院分区：
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--
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本文基于所提出的通用神经网络模型（线性延迟动态系统和有界静态非线性算子的互连）研究了一些混沌延迟神经网络的指数同步问题，并涵盖了几种著名的神经网络，如Hopfield神经网络、细胞神经网络（CNN）、双向联想记忆（BAM）网络、循环多层感知器（RMLP）。借助Lyapunov-Krasovskii稳定性理论和线性矩阵不等式(LMI)技术，推导了一些指数同步判据。使用驱动响应概念，混合反馈控制器被设计为基于这些同步标准来同步两个相同的混沌神经网络。最后，与现有结果进行详细比较并进行数值模拟，以证明所建立的同步定律的有效性。
This paper investigates the exponential synchronization problem of some chaotic delayed neural networks based on the proposed general neural network model, which is the interconnection of a linear delayed dynamic system and a bounded static nonlinear operator, and covers several well-known neural networks, such as Hopfield neural networks, cellular neural networks (CNNs), bidirectional associative memory (BAM) networks, recurrent multilayer perceptrons (RMLPs). By virtue of Lyapunov-Krasovskii stability theory and linear matrix inequality (LMI) technique, some exponential synchronization criteria are derived. Using the drive-response concept, hybrid feedback controllers are designed to synchronize two identical chaotic neural networks based on those synchronization criteria. Finally, detailed comparisons with existing results are made and numerical simulations are carried out to demonstrate the effectiveness of the established synchronization laws.
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