Abstract

This paper investigates the problem of mean-square asymptotic stability of uncertain neural networks with time-varying delay and stochastic noise. Based on generalized Finsler lemma and the linear matrix inequality (LMI) optimization technique, an improved delay-dependent stability criterion is developed. It is shown that the new stability criterion is less conservative and less computationally complex than the existing stability conditions. A numerical example is presented to substantiate the effectiveness of the theoretical results.

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 call

Disclaimer: 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.