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首页> 外文会议>International Symposium on Neural Networks pt.1; 20040819-20040821; Dalian; CN >On Robust Stability of BAM Neural Networks with Constant Delays
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On Robust Stability of BAM Neural Networks with Constant Delays

机译：具有恒定时滞的BAM神经网络的鲁棒稳定性

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摘要

The problems of determining the robust stability of bidirectional associative memory neural networks with delays are investigated in this paper. An approach combining the Lyapunov-Krasovskii stability theorem with the linear matrix inequality (LMI) technique is taken to study the problems, which provide bounds on the interconnection matrix and the activation functions. Some criteria for the robust stability, which give information on the delay-independence property, are derived. The results obtained in this paper provide one more set of easily verified guidelines for determining the robust stability of delayed BAM (DBAM) neural networks, which are less conservative and less restrictive than the ones reported recently in the literature. Some typical examples are presented to show the effectiveness of results.
机译：研究了确定具有时滞的双向联想记忆神经网络的鲁棒稳定性的问题。本文采用Lyapunov-Krasovskii稳定性定理和线性矩阵不等式（LMI）技术相结合的方法来研究这些问题，这些问题为互连矩阵和激活函数提供了边界。得出了一些鲁棒稳定性的标准，这些标准给出了时延独立性的信息。本文获得的结果为确定延迟BAM（DBAM）神经网络的鲁棒稳定性提供了另一组易于验证的准则，这些准则比最近文献报道的保守性和限制性较小。提出了一些典型的例子来说明结果的有效性。

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