| 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] |
|
| 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 2236 全文点击 984 投稿时间：2018-09-13 修订日期：2019-03-26 |
| 查看全文 查看/发表评论 下载PDF阅读器 |
| 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 |
|
| 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. |