Design of passive filters for time-delay neural networks with quantized output

Passive filtering of neural networks with time-invariant delay and quantized output is considered. A criterion on the passivity of a filtering error system is proposed by means of the Lyapunov–Krasovskii functional and the Bessel–Legendre inequality. Based on the criterion, a design approach for desired passive filters is developed in terms of the feasible solution of a set of linear matrix inequalities. Then, analyses and syntheses are extended to the time-variant delay situation using the reciprocally convex combination inequality. Finally, a numerical example with simulations is used to illustrate the applicability and reduced conservatism of the present passive filter design approaches.