State estimation of T–S fuzzy Markovian generalized neural networks with reaction–diffusion terms: a time-varying nonfragile proportional retarded sampled-data control scheme-Reference-Cited by-同舟云学术

State estimation of T–S fuzzy Markovian generalized neural networks with reaction–diffusion terms: a time-varying nonfragile proportional retarded sampled-data control scheme

Published:2020-03-11 Issue:18 Volume:32 Page:14639-14653
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Song Xiaona^ORCID,Man Jingtao,Song Shuai,Wang Zhen

Funder

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

http://link.springer.com/content/pdf/10.1007/s00521-020-04817-7.pdf

Reference48 articles.

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2. Saravanakumar R, Syed AM, Ahn CK, Karimi HR, Shi P (2017) Stability of Markovian jump generalized neural networks with interval time-varying delays. IEEE Trans Neural Netw Learn Syst 28(8):1840–1850

3. Samidurai R, Manivannan R, Ahn CK, Karimi HR (2016) New criteria for stability of generalized neural networks including Markov jump parameters and additive time delays. IEEE Trans Syst Man Cybern Syst 48(4):485–499

4. Chen G, Xia J, Zhuang G (2016) Delay-dependent stability and dissipativity analysis of generalized neural networks with Markovian jump parameters and two delay components. J Frankl Inst 353(9):2137–2158

5. Rajchakit G, Saravanakumar R (2018) Exponential stability of semi-Markovian jump generalized neural networks with interval time-varying delays. Neural Comput Appl 29(2):483–492

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