Delay-dependent Robust H2 guaranteed cost control for singular stochastic neural networks with distributed delays

This paper solves the problem of delay-dependent H₂ guaranteed cost control for singular stochastic neural networks with distributed delays. Parameter uncertainties are norm-bounded. The time-delay factors are unknown and time-varying with known bounds. The aim of this paper is to prove neural networks are stochastically asymptotically stable in the means quare for all admissible parameter uncertainties and the cost function value is not more than a specified upper bound. Based on Lyapunov stability theory and linear matrix inequalities (LMIs) techniques, a new delay-dependent stability condition is derived. Finally, a numerical example has shown the feasibility and effectiveness of the mentioned results.