引用本文: | 王婧,柏建军,薛安克.一种基于无源性理论的模糊Hopfield神经网络学习律设计方法[J].控制理论与应用,2020,37(2):405~410.[点击复制] |
WANG Jing,BAI Jian-jun,XUE An-ke.Passivity-based learning law design for a class of fuzzy Hopfield neural networks[J].Control Theory and Technology,2020,37(2):405~410.[点击复制] |
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一种基于无源性理论的模糊Hopfield神经网络学习律设计方法 |
Passivity-based learning law design for a class of fuzzy Hopfield neural networks |
摘要点击 2164 全文点击 966 投稿时间:2018-09-13 修订日期:2019-03-26 |
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DOI编号 10.7641/CTA.2019.80699 |
2020,37(2):405-410 |
中文关键词 模糊神经网络 无源性 学习律 |
英文关键词 Fuzzy neural networks Passivity Learning law |
基金项目 |
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中文摘要 |
本文研究了一类模糊Hopfield神经网络系统的稳定性问题。首先,基于无源性理论,设计了一种新的权重学习律,并通过构造的模糊Lyapunov函数证明了系统从输入到输出是无源的。在此基础上,证明了系统在该学习律下是输入到状态稳定的。相比于传统的公共Lypaunov函数,本文所提的模糊Lyapunov函数能保证系统具有更好的性能。最后,通过数值仿真验证了所提方法的有效性。 |
英文摘要 |
The stability problem of a class of fuzzy Hopfield neural networks is investigated in this paper. Based on the passivity theory, a new learning law is proposed to guarantee the system to be input-to-output passive by constructing a new fuzzy Lyapunov function, which will allow the system a better performance compared to the common Lyapunov function. Then the system is proved to be input-to-state stable by using the new learning law. Finally, a numerical example is given to show the effectiveness of the proposed approach. |