Filtering of Discrete-Time Switched Neural Networks Ensuring Exponential Dissipative and $l_{2}$ – $l_{\infty }$ Performances
Hyun Duck Choi, Choon Ki Ahn, Hamid Reza Karimi, Myo Taeg Lim · IEEE Transactions on Cybernetics · 2017
This paper studies delay-dependent exponential dissipative and l2-l∞filtering problems for discrete-time switched neural networks (DSNNs) including time-delayed states. By introducing a novel discrete-time inequality, which is a discrete-time version of the continuous-time Wirtinger-type inequality, we establish new sets of linear matrix inequality (LMI) criteria such that discrete-time filtering error systems are exponentially stable with guaranteed performances in the exponential dissipative and l2-l∞senses. The design of the desired exponential dissipative and l2-l∞filters for DSNNs can be achieved by solving the proposed sets of LMI conditions. Via numerical simulation results, we show the validity of the desired discrete-time filter design approach.