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State estimation for fuzzy cellular neural networks with time delay in the leakage term, discrete and unbounded distributed delays

机译:具有泄漏项下的时滞,离散和无界分布时滞的模糊细胞神经网络的状态估计

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This paper deals with the problem of state estimation for fuzzy cellular neural networks (FCNNs) with time delay in the leakage term, discrete and unbounded distributed delays. In this paper, leakage delay in the leakage term is used to unstable the neuron states. It is challenging to develop a delay dependent condition to estimate the unstable neuron states through available output measurements such that the error-state system is globally asymptotically stable. By constructing the Lyapunov-Krasovskii functional which contains a triple-integral term, an improved delay-dependent stability criterion is derived in terms of linear matrix inequalities (LMIs). However, by using the free-weighting matrices method, a simple and efficient criterion is derived in terms of LMIs for estimation. The restriction such as the time-varying delay which was required to be differentiable or even its time-derivative which was assumed to be smaller than one, are removed. Instead, the time-varying delay is only assumed to be bounded. Finally, numerical examples and its simulations are given to demonstrate the effectiveness of the derived results.
机译:本文研究了具有泄漏项时滞,离散和无界分布时滞的模糊细胞神经网络(FCNN)的状态估计问题。在本文中,泄漏项中的泄漏延迟用于使神经元状态不稳定。开发一种依赖于延迟的条件以通过可用的输出测量值来估计不稳定的神经元状态是具有挑战性的,从而使误差状态系统全局渐近稳定。通过构造包含三重积分项的Lyapunov-Krasovskii泛函,可以根据线性矩阵不等式(LMI)得出改进的依赖于延迟的稳定性准则。但是,通过使用自由加权矩阵方法,可以根据LMI导出简单有效的准则进行估计。消除了诸如时变延迟等要求微分的限制,或者甚至拒绝了它的时间导数(假设其小于1)的限制。相反,时变延迟仅被假定为有界。最后,通过数值算例及其仿真证明了所得结果的有效性。

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