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	Recurrent Neural Dynamics Models for Perturbed Nonstationary Quadratic Programs: A Control-Theoretical Perspective
	Qi, Yimeng1; Jin, Long1; Luo, Xin2,3,4; Zhou, MengChu5,6
	2021-01-14
摘要	Recent decades have witnessed a trend that control-theoretical techniques are widely leveraged in various areas, e.g., design and analysis of computational models. Computational methods can be modeled as a controller and searching the equilibrium point of a dynamical system is identical to solving an algebraic equation. Thus, absorbing mature technologies in control theory and integrating it with neural dynamics models can lead to new achievements. This work makes progress along this direction by applying control-theoretical techniques to construct new recurrent neural dynamics for manipulating a perturbed nonstationary quadratic program (QP) with time-varying parameters considered. Specifically, to break the limitations of existing continuous-time models in handling nonstationary problems, a discrete recurrent neural dynamics model is proposed to robustly deal with noise. This work shows how iterative computational methods for solving nonstationary QP can be revisited, designed, and analyzed in a control framework. A modified Newton iteration model and an improved gradient-based neural dynamics are established by referring to the superior structural technology of the presented recurrent neural dynamics, where the chief breakthrough is their excellent convergence and robustness over the traditional models. Numerical experiments are conducted to show the eminence of the proposed models in solving perturbed nonstationary QP.
关键词	Computational modeling Mathematical model Neural networks Control theory Analytical models Real-time systems Numerical models Control-theoretical techniques perturbed nonstationary quadratic program (QP) recurrent neural dynamics robustness theoretical analysis
DOI	10.1109/TNNLS.2020.3041364
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN	2162-237X
页码	12
通讯作者	Jin, Long(jinlongsysu@foxmail.com)
收录类别	SCI
WOS记录号	WOS:000732213500001
语种	英语