Shen, Y.Wu, Z.Shi, P.Su, H.Huang, T.2018-08-222018-08-222018IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2018; 49(2):433-4432168-22162168-2232http://hdl.handle.net/2440/113796In this paper, an asynchronous filter is proposed for Markov jump neural networks (NNs) with time delay and quantized measurements where a logarithmic quantizer is employed. The filter and quantizer are both mode-dependent and their modes are asynchronous with that of the NN, which is described by hidden Markov models. By the Lyapunov–Krasovskii functional approach, a sufficient condition is derived and a filter is then designed such that the filtering error dynamics are stochastically mean square stable and strictly (U ,S, V )-dissipative. Finally, the effectiveness and practicability of the theoretical results are verified by two examples, including a biological network.en© 2018 IEEEAsynchronous filter; asynchronous quantization; dissipativity; hidden Markov model; Markov jump neural networks (MJNNs)Journal article003008306510.1109/TSMC.2017.27891800004576708000142-s2.0-85041648038397860Shi, P. [0000-0001-6295-0405] [0000-0001-8218-586X] [0000-0002-0864-552X] [0000-0002-1358-2367] [0000-0002-5312-5435]