$$H_{\infty }$$ Filtering for Markov Jump Neural Networks Subject to Hidden-Markov Mode Observation and Packet Dropouts via an Improved Activation Function Dividing Method-Reference-Cited by-同舟云学术

$$H_{\infty }$$ Filtering for Markov Jump Neural Networks Subject to Hidden-Markov Mode Observation and Packet Dropouts via an Improved Activation Function Dividing Method

Published:2020-01-10 Issue:2 Volume:51 Page:1939-1955
ISSN:1370-4621
Container-title:Neural Processing Letters
language:en
Short-container-title:Neural Process Lett

Author:

Li Feng,Zhao Jianrong,Song Shuai,Huang Xia,Shen Hao^ORCID

Funder

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Networks and Communications,General Neuroscience,Software

Link

http://link.springer.com/content/pdf/10.1007/s11063-019-10175-w.pdf

Reference50 articles.

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3. Wang Z, Liu Y, Fraser K, Liu X (2006) Stochastic stability of uncertain hopfield neural networks with discrete and distributed delays. Phys Lett A 354(4):288–297

4. Ahn CK (2013) State estimation for T–S fuzzy hopfield neural networks via strict output passivation of the error system. Int J General Syst 42(5):503–518

5. Manivannan R, Samidurai R, Zhu Q (2017) Further improved results on stability and dissipativity analysis of static impulsive neural networks with interval time-varying delays. J Frankl Inst 354(14):6312–6340

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