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Computational Neural Dynamics Model for Time-Variant Constrained Nonlinear Optimization Applied to Winner-Take-All Operation
Liu, Mei1,2,3; Zhang, Xiaoyan1,2,3; Shang, Mingsheng3
2022-09-01
摘要Although time-variant nonlinear optimization with equality and inequality constraints (TVNOEIC) is a widespread problem in real life, the research on this issue is much less than the time-invariant one. Large lagging errors may not be inevitable if the traditional models designed for time-invariant optimizations are exploited to time-variant ones. To eliminate the lagging errors, models for handling time-variant optimization problems are urgently required. Recently, neural networks and the related neural dynamics approaches have witnessed rapid developments and made significant contributions to the online solution of various problems. On this basis, a noise-suppressing discrete-time neural dynamics (NSDTND) model is proposed in this article for solving the TVNOEIC problem. Theoretical analysis is presented to prove that the residual error of the proposed model is tiny enough to estimate the actual solution even in the presence of noise perturbation. In addition, numerical simulations, including an application on winner-take-all operation, are provided to further demonstrate the effectiveness and robustness of the proposed NSDTND model.
关键词Optimization Mathematical models Computational modeling Neural networks Numerical models Informatics Vehicle dynamics Equality and inequality constraints noise-suppressing discrete-time neural dynamics (NSDTND) model time-variant nonlinear optimization
DOI10.1109/TII.2021.3138794
发表期刊IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
ISSN1551-3203
卷号18期号:9页码:5936-5948
通讯作者Shang, Mingsheng(msshang@cigit.ac.cn)
收录类别SCI
WOS记录号WOS:000811603400024
语种英语