Polynomial dissipativity of proportional delayed BAM neural networks  

作　　者：Lin Xing Liqun Zhou 

机构地区：[1]School of Mathematics Science,Tianjin Normal University,Tianjin 300387,P.R.China

出　　处：《International Journal of Biomathematics》2020年第6期179-198,共20页生物数学学报（英文版）

基　　金：This work is supported by the National Science Foundation of Tianjin(No.18JCYBJC85800);Innovation Project for Young and Middle-aged Key Teachers in Tianjin Universities(No.135205GC38).

摘　　要：This paper pays close attention to the global polynomial dissipativity(GPD)for proportional delayed BAM neural networks(PDBAMNNs).The global exponential dissipativity(GED)and the global dissipativity(GD)are also talked about.Under the help of novel Lyapunov functionals and a generalized Halanay inequality,a set of dissipative criteria for such systems are led out,together with the global polynomial attracting set(GPAS)and the global attracting set(GAS).Further,the relationship among GPD,GED and GD is unveiled.Finally,a proposed theoretical condition is validated through a simulation experiment.

关 键 词：Global polynomial dissipativity BAM neural networks proportional delay a generalized Halanay inequality Lyapunov functionals 

分 类 号：O17[理学—数学]