R. SaravanakumarGrienggrai RajchakitChoon Ki AhnHamid Reza KarimiMahidol UniversityMaejo UniversityKorea UniversityPolitecnico di Milano2018-12-212019-03-142018-12-212019-03-142017-07-13IEEE Transactions on Systems, Man, and Cybernetics: Systems. (2017)21682232216822162-s2.0-85028950307https://repository.li.mahidol.ac.th/handle/20.500.14594/42334IEEE In this paper, we analyze the exponential stability, passivity, and (Q,S,R)-ɣ-dissipativity of generalized neural networks (GNNs) including mixed time-varying delays in state vectors. Novel exponential stability, passivity, and (Q,S,R)-ɣ-dissipativity criteria are developed in the form of linear matrix inequalities for continuous-time GNNs by constructing an appropriate Lyapunov-Krasovskii functional (LKF) and applying a new weighted integral inequality for handling integral terms in the time derivative of the established LKF for both single and double integrals. Some special cases are also discussed. The superiority of employing the method presented in this paper over some existing methods is verified by numerical examples.Mahidol UniversityComputer ScienceEngineeringArticle in PressSCOPUS10.1109/TSMC.2017.2719899