Abstract

This paper investigates the robust dissipative state estimation (RDSE) methodology for fuzzy stochastic neural networks (FSNNs) with time-varying delays. The Takagi–Sugeno (T–S) fuzzy model representation is established to the dissipative state estimator design of FSNNs. Through Lyapunov stability theory and linear matrix inequality (LMIs) technique, sufficient conditions are established. Then, a new delay-dependent RDSE criterion is derived by applying novel stochastic double integral inequality and convex combination approach. Finally, a numerical example is provided to illustrate that the proposed approach is effective for delayed FSNNs.

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.