Abstract
This paper is devoted to studying noise-to-state practical stability and stabilization problems for random neural networks in the presence of general disturbances. It is proved that the existence and uniqueness of solutions is ensured if the noise intensity function is locally Lipschitz. Using random Lyapunov theory and the existence of practical Lyapunov functions, criteria are established for noise-to-state practical stability in mean of random neural networks. Some easily checkable and computable conditions are provided based on the structure characterization of the neural networks. Numerical examples are given to demonstrate the effectiveness of the developed methods.
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.
