title: Robust stability of Cohen-Grossberg neural networks via state transmission matrix.
abstract: This brief is concerned with the global robust exponential stability of a class of interval Cohen-Grossberg neural networks with both multiple time-varying delays and continuously distributed delays. Some new sufficient robust stability conditions are established in the form of state transmission matrix, which are different from the existing ones. Furthermore, a sufficient condition is also established to guarantee the global stability for this class of Cohen-Grossberg neural networks without uncertainties. Three examples are used to show the effectiveness of the obtained results.
authors.affiliation: School of Information Science and Engineering, Northeastern University, Shenyang, Liaoning 110004, China. zhanshan_wang@163.com
authors.forename: Zhanshan
authors.initials: Z
authors.lastname: Wang
authors.forename: Huaguang
authors.initials: H
authors.lastname: Zhang
authors.forename: Wen
authors.initials: W
authors.lastname: Yu
mesh_terms: D000465:Algorithms; D016571:Neural Networks, Computer; D013997:Time Factors