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Noise-Suppressing Neural Dynamics for Time-Dependent Constrained Nonlinear Optimization With Applications
Wei, Lin1; Jin, Long1; Luo, Xin2,3
2022-01-05
摘要Up to date, the existing methods for nonlinear optimization with time-dependent parameters can be classified into two types: 1) static methods are capable of handling inequality constraints but may generate large lagging errors in the solution of the intrinsically time-dependent constrained nonlinear optimization (TDCNO) problem due to the hypothesis of short-time invariance and 2) time-variant methods, e.g., zeroing neural networks, are able to remedy the lagging error but fail to solve the TDCNO problem under inequality constraints. To resolve this contradiction, a noise-suppressing neural dynamics (NSND) model is proposed to solve the TDCNO problem subject to both equality and inequality constraints via the nonlinear complementary problem (NCP) function. The proposed method allows inequality constraints for unknown variables, removes the short-time invariance hypothesis, and further eliminates lagging errors during the solving process in the presence of noises. Besides, the rapid convergence, global stability, and noise processing of the NSND model are verified by the theoretical analyses. Simulation results of illustrative examples, including dimensionality reduction on principal component analyses (PCA) and a robot motion control, show that the NSND model outperforms the existing models for the TDCNO problem.
关键词Optimization Mathematical models Analytical models Convergence Principal component analysis Time-varying systems Nonlinear equations Constrained nonlinear optimization dimensionality reduction on principal component analyses (PCA) equality and inequality constraints neural dynamics noise suppression robot arm control
DOI10.1109/TSMC.2021.3138550
发表期刊IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
ISSN2168-2216
页码12
通讯作者Jin, Long(longjin@ieee.org) ; Luo, Xin(luoxin21@cigit.ac.cn)
收录类别SCI
WOS记录号WOS:000740072100001
语种英语