Extended dissipative state estimation of delayed stochastic neural networks

Abstract In this article, extended dissipative state estimation (EDSE) criteria is established for stochastic neural networks with variable delay states. The EDSE criteria includes passivity state estimation, dissipativity state estimation, H∞ state estimation and L 2 − L ∞ state estimation performances. Sufficient conditions for EDSE criteria are established by choosing Lyapunov–Krasovskii functionals (LKF) and using linear matrix inequality (LMIs) technique. New stochastic double integral inequality is applied for the integral term in the derivative of the chosen LKF in main results, which helps to establish EDSE criterion for delayed stochastic neural networks. The feasibility and superiority of the proposed approach is shown by providing numerical examples.