Novel Global Asymptotic Stability and Dissipativity Criteria of BAM Neural Networks With Delays - Liu, Mei; Jiang, Haijun; Hu, Cheng; Lu, Binglong; Li, Zhanfeng (Frontiers in Physics, 2022)
In this paper, issues of both stability and dissipativity for a class of bidirectional associative memory (BAM) neural networks with time delays are investigated. By using generalized Halanay inequalities and constructing suitable Lyapunov functional, some new criteria are obtained for the global asymptotic stability of BAM neural networks with time delays. And without assuming differentiability and boundedness of activation functions, some new sufficient conditions for checking the dissipativity of the addressed neural networks are established by using matrix theory, inner product properties. The derived results extend and improve some previous known works on these problems for conventional BAM neural networks. Finally, numerical examples and their simulations are given to show the effectiveness of the theoretical results.