Robust partially mode-dependent H∞ filtering for discrete-time nonhomogeneous Markovian jump neural networks with additive gain perturbations

This paper studies the robust partially mode-dependent H-infinity filtering for nonhomogeneous Markovian jump neural networks with additive gain perturbations. The discrete time-varying jump transition probability matrix is considered to be a polytope set. A partially mode-dependent filter with additive gain perturbations is constructed to increase the robustness of the filter, which is subjects to H-infinity performance index. Based on the Lyapunov function approach, sufficient conditions are established such that the filtering error system is robustly stochastically stable. The efficiency of the new technique is illustrated by an illustrative example and a biological network example.