Exponential passive filtering of neutral-type neural networks with time-varying discrete and distributed delays

The exponential passive filtering problem is studied for neutral-type neural networks with time-varying discrete and distributed delays. Based on the passive theory, the sufficient condition for the existence of the exponential passive filter is given. By introducing an appropriate Lyapunov-Krasovskii functional and using Jensen´s inequality techniques to deal with its derivative, the criterion which ensures error dynamic system to be strictly exponentially passive is presented in the form of nonlinear matrix inequality. In order to solve the nonlinear problem, a cone complementarity linearization (CCL) algorithm is proposed. An example is given to demonstrate the effectiveness of the proposed criterion.