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首页> 外文会议>International conference on information technology and industrial engineering >Global Exponential Stability for Neutral-type Uncertain Dynamic Neural Networks With Hybrid Time-varying Delays
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Global Exponential Stability for Neutral-type Uncertain Dynamic Neural Networks With Hybrid Time-varying Delays

机译：具有混合时变延迟的中性不确定动态神经网络的全局指数稳定性

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摘要

Time delays often occur in many industrial systems, and may deteriorate system performance or even cause instability. Therefore, stability analysis is important for the systems. The global exponential stability was discussed for the neutral-type uncertain dynamic neural networks with hybrid timevarying d-elays. Without assuming the bounedness of the activation function, and the parameter uncertainties are assumed to be norm bounded. Based on the Lyapunov-Krasovskii functional stability analysis and the linear matrix inequality (LMI) approach, a new sufficient condition was derived. Which generalize the previous results in the literature and has less conservative.
机译：在许多工业系统中经常发生时滞，并且可能会降低系统性能甚至导致不稳定。因此，稳定性分析对于系统很重要。讨论了具有混合时宽D-ELay的中性不确定动态神经网络的全局指数稳定性。不假设激活函数的弹声，并且假设参数不确定性是常态的。基于Lyapunov-Krasovskii功能稳定性分析和线性矩阵不等式（LMI）方法，得到了一种新的充分条件。这在文献中概括了先前的结果，并具有较少的保守。

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