极小极大
鞍点
李雅普诺夫函数
人工神经网络
二次方程
数学
数学优化
功能(生物学)
二次函数
理论(学习稳定性)
应用数学
离散时间和连续时间
计算机科学
控制理论(社会学)
人工智能
非线性系统
控制(管理)
物理
机器学习
统计
生物
进化生物学
量子力学
几何学
作者
Xingbao Gao,Li-Zhi Liao
出处
期刊:IEEE transactions on neural networks and learning systems
[Institute of Electrical and Electronics Engineers]
日期:2024-01-01
卷期号:: 1-15
标识
DOI:10.1109/tnnls.2023.3236695
摘要
This article presents two novel continuous-and discrete-time neural networks (NNs) for solving quadratic minimax problems with linear equality constraints. These two NNs are established based on the conditions of the saddle point of the underlying function. For the two NNs, a proper Lyapunov function is constructed so that they are stable in the sense of Lyapunov, and will converge to some saddle point(s) for any starting point under some mild conditions. Compared with the existing NNs for solving quadratic minimax problems, the proposed NNs require weaker stability conditions. The validity and transient behavior of the proposed models are illustrated by some simulation results.
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