title: Delay-derivative-dependent stability for delayed neural networks with unbound distributed delay.
abstract: In this brief, based on Lyapunov-Krasovskii functional approach and appropriate integral inequality, a new sufficient condition is derived to guarantee the global stability for delayed neural networks with unbounded distributed delay, in which the improved delay-partitioning technique and general convex combination are employed. The LMI-based criterion heavily depends on both the upper and lower bounds on time delay and its derivative, which is different from the existent ones and has wider application fields than some present results. Finally, three numerical examples can illustrate the efficiency of the new method based on the reduced conservatism which can be achieved by thinning the delay interval.
authors.affiliation: School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China. litaodongrui@gmail.com
authors.forename: Tao
authors.initials: T
authors.lastname: Li
authors.forename: Aiguo
authors.initials: A
authors.lastname: Song
authors.forename: Shumin
authors.initials: S
authors.lastname: Fei
authors.forename: Ting
authors.initials: T
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D006801:Humans; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors