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Robust Synchronization for Discrete-Time Coupled Markovian Jumping Neural Networks With Mixed Time-Delays

机译:具有混合时间延迟的离散时间耦合的马尔科维亚跳跃神经网络的鲁棒同步

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摘要

This paper concerns the robust synchronization problems for discrete-time coupled neural networks with discrete time delay and distributed time delays. Inner parameters in individual neural network are subject to be uncertain and both coupled matrixes and weight matrixes are supposed to switch from one mode to another because of the markovian jumping chain. Mixed time delays contain discrete and distributed time delays and the mixed time delays not only exist in the individual neural cell, but also exist in the coupled cells. By using the novel Lyapunov-Krasovskii functional method and Kronecker product as tools, mean square stability conditions are provided in terms of linear matrix inequalities. In numerical simulations, two examples (with and without unknown parameters) are given and simulation results show the robustness and effectiveness of our methods.
机译:本文涉及具有离散时间延迟和分布时间延迟的离散时间耦合神经网络的鲁棒同步问题。各个神经网络中的内部参数受到不确定,并且耦合矩阵和重量矩阵应该由于马尔科维亚跳链,因此应该从一个模式切换到另一个模式。混合时间延迟包含离散和分布的时间延迟,并且混合时间延迟不仅存在于各个神经电池中,而且存在于耦合单元中。通过使用新颖的Lyapunov-Krasovskii功能方法和Kronecker产品作为工具,就线性矩阵不等式提供了均方稳定性条件。在数值模拟中,给出了两个示例(有没有未知参数),并且仿真结果显示了我们方法的稳健性和有效性。

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