Global stability analysis for delayed neural networks via an interval matrix approach
Academic Article
Overview
Identity
Additional Document Info
Other
View All
Overview
abstract
Global asymptotic stability for a general class of neural networks with delays is reduced to that for interval linear delayed differential equations under the assumption of Lipschitz continuity. By employing Lyapunov-Krasovskii theory, the problem is further reduced to that of Hurwitz stability of interval matrices. Based on the later theory, several new sets of stability criteria for neural networks with constant delays are derived. This demonstration and comparison with recent results show that the present results are new stability criteria for the investigated neural network model. The Institution of Engineering and Technology 2007.
