title: Stability and dissipativity analysis of static neural networks with time delay.
abstract: This paper is concerned with the problems of stability and dissipativity analysis for static neural networks (NNs) with time delay. Some improved delay-dependent stability criteria are established for static NNs with time-varying or time-invariant delay using the delay partitioning technique. Based on these criteria, several delay-dependent sufficient conditions are given to guarantee the dissipativity of static NNs with time delay. All the given results in this paper are not only dependent upon the time delay but also upon the number of delay partitions. Some examples are given to illustrate the effectiveness and reduced conservatism of the proposed results.
authors.forename: Zheng-Guang
authors.initials: ZG
authors.lastname: Wu
authors.forename: James
authors.initials: J
authors.lastname: Lam
authors.forename: Hongye
authors.initials: H
authors.lastname: Su
authors.forename: Jian
authors.initials: J
authors.lastname: Chu
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D013997:Time Factors