Abstract
This paper is concerned with studying H ∞ state estimation of memristor-based recurrent neural networks(MRNNs) with mixed delays. The MRNNs addressed is comprehensive to cover distributed-delays and time-varying delays in order to reflect the reality more closely and accurate. The delay-dependent design criteria is presented under which the resulting estimation error system is globally asymptotically stable and a prescribed performance is guaranteed in the H ∞ sense by the Jensen inequality, Wirtinger inequality and reciprocally convex approach. Finally, the simulation results confirm the effectiveness of Theorem 3.1 for the design of guaranteed performance H ∞ state estimator for MRNNs with mixed delays.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
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.