L2-L? Filtering for TakagiSugeno fuzzy neural networks based on Wirtinger-type inequalities
This paper deals with the L2L? L 2  L ? filtering problem for continuous-time TakagiSugeno 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 L2L? L 2  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.
wirtinger-type inequality linear matrix inequality (lmi) l2l? l 2  l ? filtering l2l? l 2  l ? l2l? l 2  l ? l 2  l ? takagisugeno fuzzy hopfield neural network