AbstractIn this paper, stochastic stability is analyzed for a class of discrete-time switched neural networks, in which time-varyin'/> Stochastic Stability for a Class of Discrete-time Switched Neural Networks with Stochastic Noise and Time-varying Mixed Delays
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Stochastic Stability for a Class of Discrete-time Switched Neural Networks with Stochastic Noise and Time-varying Mixed Delays

机译：随机噪声的一类离散时间切换神经网络的随机稳定性，随机噪声和时变混合延迟

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AbstractIn this paper, stochastic stability is analyzed for a class of discrete-time switched neural networks, in which time-varying mixed delays and stochastic noise are considered. Specifically, benefitting from the triple summation term included in a new Lyapunov functional, time-varying distributed delays are tackled and a criterion of decay estimation for a non-switched neural network is firstly obtained. Subsequently, in view of average dwell time methodology and stochastic analysis, several sufficient conditions are obtained to ensure that the stochastic stability problem is solvable. Furthermore, the derived sufficient conditions reflect that the decay rate of the considered neural networks has a close relationship with average dwell time, upper and lower bounds of delays and intensity of stochastic noise. Finally, validity of the inferred conclusions is given by a simulated example.]]>
机译：<！[CDATA [ <标题>抽象 ara>在本文中，分析了一类离散时间切换神经网络的随机稳定性，其中 考虑时变混合延迟和随机噪声。 具体地，从包括在新的Lyapunov功能中包括的三求术语的益处，并且首先获得对非交换神经网络的衰变估计的标准。 随后，考虑到平均停留时间方法和随机分析，获得了几种充分的条件以确保随机稳定性问题是可溶性的。 此外，衍生的充足条件反映了所考虑的神经网络的衰减率与平均停留时间，上限和下限的延迟和随机噪声强度的衰减率密切相关。 最后，通过模拟示例给出了推断结论的有效性。 ]]>

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