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 talke...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.展开更多
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).
文摘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.
