Noise-suppressing zeroing neural network for online solving time-varying nonlinear optimization problem: a control-based approach-Reference-Cited by-同舟云学术

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Noise-suppressing zeroing neural network for online solving time-varying nonlinear optimization problem: a control-based approach

Published:2019-12-06 Issue:15 Volume:32 Page:11505-11520
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Sun Zhongbo^ORCID,Shi Tian,Wei Lin,Sun Yingyi,Liu Keping,Jin Long

Funder

National Natural Science Foundation of China

the China Postdoctoral Science Foundation Funded Project

Key Science and Technology Projects of Jilin Province

the Industrial Innovation Special Funds Project of Jilin Province

the Jilin Engineering Laboratory for Intelligence Robot and Visual Measurement Technology,

the Fundamental Research Funds for the Central Universities

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

http://link.springer.com/content/pdf/10.1007/s00521-019-04639-2.pdf

Reference42 articles.

1. Yang Y, Zhang Y (2013) Superior robustness of power-sum activation functions in Zhang neural networks for time-varying quadratic programs perturbed with large implementation errors. Neural Comput Appl 22:175–185

2. Zhang Y, Yang Y, Cai B, Guo D (2012) Zhang neural network and its application to Newton iteration for matrix square root estimation. Neural Comput Appl 21:453–460

3. Andrei N (2018) An adaptive scaled BFGS method for unconstrained optimization. Numer Algorithms 77(2):413–432

4. Abubakar AB, Kumam P (2019) A descent Dai-Liao conjugate gradient method for nonlinear equations. Numer Algorithms 81(1):197–210

5. Sun ZB, Tian YT, Wang J (2018) A novel projected Fletcher–Reeves conjugate gradient approach for finite-time optimal robust controller of linear constraints optimization problem: application to bipedal walking robots. Optim Control Appl Methods 39(1):130–159

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