NDLI: New cooperative projection neural network for nonlinearly constrained variational inequality

Content Provider	Springer Nature Link
Author	Xia, YouSheng
Copyright Year	2009
Abstract	This paper proposes a new cooperative projection neural network (CPNN), which combines automatically three individual neural network models with a common projection term. As a special case, the proposed CPNN can include three recent recurrent neural networks for solving monotone variational inequality problems with limit or linear constraints, respectively. Under the monotonicity condition of the corresponding Lagrangian mapping, the proposed CPNN is theoretically guaranteed to solve monotone variational inequality problems and a class of nonmonotone variational inequality problems with linear and nonlinear constraints. Unlike the extended projection neural network, the proposed CPNN has no limitation on the initial point for global convergence. Compared with other related cooperative neural networks and numerical optimization algorithms, the proposed CPNN has a low computational complexity and requires weak convergence conditions. An application in real-time grasping force optimization and examples demonstrate good performance of the proposed CPNN.
Starting Page	1766
Ending Page	1777
Page Count	12
File Format	PDF
ISSN	10092757
Journal	Science in China Series : Information Sciences
Volume Number	52
Issue Number	10
e-ISSN	18622836
Language	English
Publisher	SP Science in China Press
Publisher Date	2009-10-06
Publisher Place	Heidelberg
Access Restriction	Subscribed
Subject Keyword	variational inequality problems general constraints cooperative recurrent neural network complexity global convergence conditions Information Systems and Communication Service
Content Type	Text
Resource Type	Article
Subject	Computer Science

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