A Variance-Constrained Method to Protocol-Based $$H_{\infty }$$ State Estimation for Delayed Neural Networks with Randomly Occurring Sensor Nonlinearity-Reference-Cited by-同舟云学术

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A Variance-Constrained Method to Protocol-Based $$H_{\infty }$$ State Estimation for Delayed Neural Networks with Randomly Occurring Sensor Nonlinearity

Published:2023-11-01 Issue:9 Volume:55 Page:12501-12523
ISSN:1370-4621
Container-title:Neural Processing Letters
language:en
Short-container-title:Neural Process Lett

Author:

Gao Yan,Hu Jun,Chen Cai,Yu Hui,Jia Chaoqing

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

https://link.springer.com/content/pdf/10.1007/s11063-023-11430-x.pdf

Reference49 articles.

1. Benouar S, Hafid A, Kedir-Talha M, Seoane F (2021) Classification of impedance cardiography dz/dt complex subtypes using pattern recognition artificial neural networks. Biomed Eng Biomed Tech 66(5):515–527

2. Lin A, Cheng J, Park JH, Yan H, Qi W (2023) Fault detection filtering of nonhomogeneous Markov switching memristive neural networks with output quantization. Inf Sci 632:715–729

3. Wang M, Wang H, Zheng H (2022) A mini review of node centrality metrics in biological networks. Int J Netw Dyn Intell 1(1):99–110

4. Singh MP, Saraswat VK (2017) Multilayer feed forward neural networks for non-linear continuous bidirectional associative memory. Appl Soft Comput 61:700–713

5. Onomi T, Maenami Y, Nakajima K (2011) Superconducting neural network for solving a combinatorial optimization problem. IEEE Trans Appl Supercond 21(3):701–704

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