Estimating the ultimate bound and positively invariant set for a class of Hopfield networks.

IEEE transactions on neural networks Pub Date : 2011-11-01 Epub Date: 2011-09-26 DOI:10.1109/TNN.2011.2166275
Jianxiong Zhang, Wansheng Tang, Pengsheng Zheng
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引用次数: 1

Abstract

In this paper, we investigate the ultimate bound and positively invariant set for a class of Hopfield neural networks (HNNs) based on the Lyapunov stability criterion and Lagrange multiplier method. It is shown that a hyperelliptic estimate of the ultimate bound and positively invariant set for the HNNs can be calculated by solving a linear matrix inequality (LMI). Furthermore, the global stability of the unique equilibrium and the instability region of the HNNs are analyzed, respectively. Finally, the most accurate estimate of the ultimate bound and positively invariant set can be derived by solving the corresponding optimization problems involving the LMI constraints. Some numerical examples are given to illustrate the effectiveness of the proposed results.

一类Hopfield网络的极限界和正不变集的估计。
本文基于Lyapunov稳定性判据和Lagrange乘子方法研究了一类Hopfield神经网络的极限界和正不变集。通过求解线性矩阵不等式(LMI),得到了hnn的极限界和正不变集的超椭圆估计。在此基础上,分析了hnn的唯一平衡点的全局稳定性和不稳定性区域。最后,通过求解相应的涉及LMI约束的优化问题,得到了最终界和正不变集的最精确估计。数值算例说明了所提结果的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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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0.00%
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2
审稿时长
8.7 months
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