Asynchronous Filtering for Markov Jump Neural Networks With Quantized Outputs Academic Article uri icon

abstract

  • © 2018 IEEE. In 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.

author list (cited authors)

  • Shen, Y., Wu, Z., Shi, P., Su, H., & Huang, T.

citation count

  • 44

publication date

  • January 2018