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Input-to-State Stability for Stochastic Delay Neural Networks with Markovian Switching

机译:随机延迟神经网络与马尔可夫交换的输入到状态稳定性

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摘要

In this paper, some problems on the input-to-state stability, integral input-to-state stability, and stochastic input-to-state stability of stochastic non-autonomous neural networks with time-varying delay and Markovian switching are investigated. By using the generalized integral inequality, the Lyapunov function approach and the stochastic analysis theory, the input-to-state stability, integral input-to-state stability, and stochastic input-to-state stability for such neural networks are discussed when the time-varying delay is a bounded function. The integral input-to-state stability and stochastic input-to-state stability are also implied. One example is given to illustrate the derived theoretical result.
机译:在本文中,研究了对具有时变延迟和马尔可夫切换的随机非自治神经网络的输入到状态稳定性,积分输入到状态稳定性以及随机输入到状态稳定性的一些问题。 通过使用广义的整体不等式,Lyapunov函数方法和随机分析理论,输入到状态稳定性,积分输入到状态稳定性以及这种神经网络的随机输入到状态稳定性 - 延迟是有界函数。 还暗示积分输入到状态稳定性和随机输入到状态稳定性。 给出一个示例来说明导出的理论结果。

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