L2-L∞ Filtering for Takagi–Sugeno fuzzy neural networks based on Wirtinger-type inequalities

This paper deals with the L2–L∞L2–L∞ filtering problem for continuous-time Takagi–Sugeno fuzzy delayed Hopfield neural networks based on Wirtinger-type inequalities. A new set of delay-dependent conditions is established to estimate the state variables of fuzzy neural networks through the observed input and output variables. This ensures that the state estimation error system is asymptotically stable with a guaranteed L2–L∞L2–L∞ performance. The presented criterion is formulated in terms of linear matrix inequalities (LMIs). An example with simulation results is given to illustrate the effectiveness of the proposed fuzzy neural state estimator.