Abstract
This paper studies the problem of stability analysis for discrete-time recurrent neural networks (DRNNs) with time-varying delays. Under a weak assumption on the activation functions, by defining a more general type of Lyapunov functionals and using a convex combination technique, a new delay-dependent stability criterion is proposed to guarantee the stability and uniqueness of equilibrium point of DRNNs in terms of linear matrix inequalities (LMIs). Compared with the existing results, the newly obtained stability condition is less conservative. A numerical example is given to illustrate the effectiveness and the benefits of the proposed method.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
