Robust H ∞ performance for uncertain stochastic switched inertial neural networks with time-varying delay under a novel state-dependent switching law
K. Banupriya, A. Nasira Banu, V. Dhanya · International Journal of Computer Mathematics · 2025
In this paper, the robust H∞ performance-based stability problem is investigated for stochastic switched inertial neural networks (SSINNs) with time-varying parametric uncertainties. First, the original system can be transformed into a first-order system by selecting an appropriate variable substitution. Moreover, H∞ performance-based criteria are obtained as linear matrix inequalities (LMIs) to ensure robust asymptotic stability for SSINNs with disturbance attenuation level γ>0 about its equilibrium point for all admissible uncertainties with time-varying delays. These criteria are derived using Jensen's inequality, a proper Lyapunov-Krasovskii functional (LKF) theory, and the state-dependent switching (SDS) law approach. The proposed SDS law has been shown to guarantee the H∞ performance standards for the system composed entirely of unstable subsystems. Additionally, the gains are derived by solving a set of LMIs and are readily verifiable with common numerical tools. Lastly, two simulation-based examples are offered to show the viability of the suggested H∞ performance method.