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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Estimator Design for Discrete-Time Switched Neural Networks With Asynchronous Switching and Time-Varying Delay
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Estimator Design for Discrete-Time Switched Neural Networks With Asynchronous Switching and Time-Varying Delay

机译：具有异步切换和时变时滞的离散时间切换神经网络的估计器设计

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摘要

This brief deals with the estimator design problem for discrete-time switched neural networks with time-varying delay. One main problem is the asynchronous-mode switching between the neuron state and the estimator. Our goal is to design a mode-dependent estimator for the switched neural networks under average dwell time switching such that the estimation error system is exponentially stable with a prescribed l2 gain (in the H∞ sense) from the noise signal to the estimation error. A new Lyapunov functional is constructed that may increase during the mismatched switchings. New results on the stability and l2 gain analysis are then obtained. The admissible estimator gains are computed by solving a set of linear matrix inequalities. The relations among the switching law, the maximal delay upper bound, and the optimal H∞ disturbance attenuation level are established. The effectiveness of the proposed design method is finally illustrated by a numerical example.
机译：本摘要介绍了具有时变时滞的离散时间切换神经网络的估计器设计问题。一个主要问题是神经元状态和估计器之间的异步模式切换。我们的目标是为平均停留时间切换下的开关神经网络设计一个与模式相关的估计器，以使估计误差系统具有规定的l 2 增益（在H ∞ 感）从噪声信号到估计误差。构建了新的Lyapunov功能，该功能在不匹配的切换过程中可能会增加。然后获得了稳定性和l 2 增益分析的新结果。通过求解一组线性矩阵不等式来计算可允许的估计器增益。建立了切换规律，最大时延上限和最优H ∞干扰衰减水平之间的关系。最后通过数值例子说明了所提出设计方法的有效性。

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