Global exponential p-norm stability of BAM neural networks with unbounded time-varying delays:A method based on the representation of solutions  

作　　者：Xi Chen Tingting Yu Xian Zhang 

机构地区：[1]School of Mathematical Science Heilongjiang University,No.74 Xuefu Road Harbin 150080,P.R.China [2]Heilongjiang Provincial Key Laboratory of the Theory and Computation of Compler Systems Heilongjiang University,No.74 Xuefu Road Harbin 150080,P.R.China

出　　处：《International Journal of Biomathematics》2023年第5期71-86,共16页生物数学学报（英文版）

基　　金：supported in part by the Natural Science Foundation of Heilongjiang Province (No.YQ2021F014);the Fundamental Research Funds for the provincial universities of Heilongjiang Province (No.2020-KYYWF-1040)。

摘　　要：This paper studies the global exponential p-norm stability of bidirectional associative memory(BAM)neural networks with unbounded time-varying delays.A novel method based on the representation of solutions is put forward to deduce a global exponential p-norm stability criterion.This method does not need to set up any Lyapunov-Krasovskii functionals(LKF),which can greatly reduce a large amount of computations and is simpler than the existing methods.In the end,representative numerical examples are given to llustrate the availability of the method.

关 键 词：BAM neural networks global exponential p-norm stability unbounded timevarying delays representation of solutions 

分 类 号：O17[理学—数学]