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随机时滞神经网络的稳定性与L2性能分析

Stability and L2 performance analysis of stochastic delayed neural networks.

作者信息

Chen Yun, Zheng Wei Xing

机构信息

School of Computing and Mathematics, University of Western Sydney, Penrith NSW 2751, Australia.

出版信息

IEEE Trans Neural Netw. 2011 Oct;22(10):1662-8. doi: 10.1109/TNN.2011.2163319. Epub 2011 Aug 12.

Abstract

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.

摘要

本简报聚焦于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。基于适用于随机系统的延迟分区Lyapunov-Krasovskii泛函方法和广义Finsler引理,开发了新的均方指数稳定性和(L(2))性能准则。所建立的分析结果不涉及任何模型变换、交叉项估计、额外的自由加权矩阵或调谐参数。给出了数值例子,以验证所提方法比现有方法保守性更低且计算复杂度更小。

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