Global Dissipativity on Uncertain Discrete‐Time Neural Networks with Time‐Varying Delays
Qiankun Song, Jinde Cao · Discrete Dynamics in Nature and Society · 2010
The problems on global dissipativity and global exponential dissipativity are investigated for uncertain discrete‐time neural networks with time‐varying delays and general activation functions. By constructing appropriate Lyapunov‐Krasovskii functionals and employing linear matrix inequality technique, several new delay‐dependent criteria for checking the global dissipativity and global exponential dissipativity of the addressed neural networks are established in linear matrix inequality (LMI), which can be checked numerically using the effective LMI toolbox in MATLAB. Illustrated examples are given to show the effectiveness of the proposed criteria. It is noteworthy that because neither model transformation nor free‐weighting matrices are employed to deal with cross terms in the derivation of the dissipativity criteria, the obtained results are less conservative and more computationally efficient.