随机延迟神经网络的稳定性和L2性能分析。

IEEE transactions on neural networks Pub Date : 2011-10-01 Epub Date: 2011-08-12 DOI:10.1109/TNN.2011.2163319
Yun Chen, Wei Xing Zheng
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引用次数: 28

摘要

本文主要研究一类受加性和乘性随机噪声干扰的不确定时滞神经网络的鲁棒均方指数稳定性和L(2)性能分析。基于延迟划分Lyapunov-Krasovskii泛函方法和适用于随机系统的广义Finsler引理,提出了新的均方指数稳定性和L(2)性能准则。分析结果的建立不涉及任何模型转换,交叉项的估计,额外的自由加权矩阵,或调整参数。数值算例表明,该方法具有较低的保守性和较低的计算复杂度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Stability and L2 performance analysis of stochastic delayed neural networks.

This brief focuses on the robust mean-square exponential stability and L(2) performance analysis for a class of uncertain time-delay neural networks perturbed by both additive and multiplicative stochastic noises. New mean-square exponential stability and L(2) performance criteria are developed based on the delay partition Lyapunov-Krasovskii functional method and generalized Finsler lemma which is applicable to stochastic systems. The analytical results are established without involving any model transformation, estimation for cross terms, additional free-weighting matrices, or tuning parameters. Numerical examples are presented to verify that the proposed approach is both less conservative and less computationally complex than the existing ones.

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来源期刊
IEEE transactions on neural networks
IEEE transactions on neural networks 工程技术-工程:电子与电气
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审稿时长
8.7 months
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