ABSTRACT In this paper, we study the robust H ∞ performance for discrete-time T-S fuzzy switched memristive stochastic neural networks with mixed time-varying delays and switching signal design. The neural network under consideration is subject to time-varying and norm bounded parameter uncertainties. Decomposing of the delay interval approach is employed in both the discrete delays and distributed delays. By constructing a proper Lyapunov-Krasovskii functional (LKF) with triple summation terms and using an improved summation inequality techniques. Sufficient conditions are derived in terms of linear matrix inequalities (LMIs) to guarantee the considered discrete-time neural networks to be exponentially stable. Finally, numerical examples with simulation results are given to illustrate the effectiveness of the developed theoretical results.

中文翻译：

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具有混合时变延迟的离散时间 TS 模糊切换忆阻随机神经网络的鲁棒 H∞ 性能

摘要 在本文中，我们研究了具有混合时变延迟和开关信号设计的离散时间 TS 模糊切换忆阻随机神经网络的鲁棒 H ∞ 性能。所考虑的神经网络受到时变和范数有界参数不确定性的影响。离散延迟和分布式延迟均采用延迟间隔分解法。通过构造具有三重求和项的适当 Lyapunov-Krasovskii 泛函 (LKF) 并使用改进的求和不等式技术。根据线性矩阵不等式 (LMI) 推导出足够的条件，以保证所考虑的离散时间神经网络呈指数稳定。最后，给出带有仿真结果的数值例子来说明所开发的理论结果的有效性。