首页> 外文会议>IFIP TC 7 conference on system modeling and optimization >A Criterion for Robust Stability with Respect to Parametric Uncertainties Modeled by Multiplicative White Noise with Unknown Intensity, with Applications to Stability of Neural Networks
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A Criterion for Robust Stability with Respect to Parametric Uncertainties Modeled by Multiplicative White Noise with Unknown Intensity, with Applications to Stability of Neural Networks

机译:关于由不确定强度乘以白噪声建模的参数不确定性的鲁棒稳定性的准则,及其在神经网络稳定性中的应用

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摘要

In the present paper a robust stabilization problem of continuous-time linear dynamic systems with Markov jumps and corrupted with multiplicative (state-dependent) white noise perturbations is considered. The robustness analysis is performed with respect to the intensity of the white noises. It is proved that the robustness radius depends on the solution of an algebraic system of coupled Lyapunov matrix equations.
机译:在本文中,考虑了具有马尔可夫跳变并被乘性(取决于状态)的白噪声扰动破坏的连续时间线性动力系统的鲁棒镇定问题。针对白噪声的强度执行鲁棒性分析。证明了鲁棒性半径取决于耦合的Lyapunov矩阵方程的代数系统的解。

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