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

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.

authors

• Huang, Tingwen

published proceedings

• IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS

author list (cited authors)

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

citation count

• 79

complete list of authors

• Shen, Ying||Wu, Zheng-Guang||Shi, Peng||Su, Hongye||Huang, Tingwen

publication date

• February 2019

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• IEEE Transactions on Systems, Man, and Cybernetics: Systems  Journal

keywords

• Asynchronous Filter
• Asynchronous Quantization
• Dissipativity
• Hidden Markov Model
• Markov Jump Neural Networks (mjnns)

Digital Object Identifier (DOI)

• 10.1109/TSMC.2017.2789180

start page

• 433

end page

• 443

volume

• 49

issue

• 2

URL

• http://dx.doi.org/10.1109/tsmc.2017.2789180