A high performance neural network for solving nonlinear programming problems with hybrid constraints
Tao, Q; Cao, JD; Xue, MS; Qiao, H
Source PublicationPHYSICS LETTERS A
2001-09-17
Volume288Issue:2Pages:88-94
AbstractA continuous neural network is proposed in this Letter for solving optimization problems. It not only can solve nonlinear programming problems with the constraints of equality and inequality, but also has a higher performance. The main advantage of the network is that it is an extension of Newton's gradient method for constrained problems, the dynamic behavior of the network under special constraints and the convergence rate can be investigated. Furthermore, the proposed network is simpler than the existing networks even for solving positive definite quadratic programming problems. The network considered is constrained by a projection operator on a convex set. The advanced performance of the proposed network is demonstrated by means of simulation of several numerical examples. (C) 2001 Elsevier Science B.V. All rights reserved.
KeywordNonlinear Programming Problem Neural Network Energy Function Gradient Method Global Asymptotic Stability
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12581
Collection复杂系统管理与控制国家重点实验室_机器人理论与应用
Corresponding AuthorTao, Q
AffiliationUniv Sci & Technol China, Dept Automat
Recommended Citation
GB/T 7714
Tao, Q,Cao, JD,Xue, MS,et al. A high performance neural network for solving nonlinear programming problems with hybrid constraints[J]. PHYSICS LETTERS A,2001,288(2):88-94.
APA Tao, Q,Cao, JD,Xue, MS,&Qiao, H.(2001).A high performance neural network for solving nonlinear programming problems with hybrid constraints.PHYSICS LETTERS A,288(2),88-94.
MLA Tao, Q,et al."A high performance neural network for solving nonlinear programming problems with hybrid constraints".PHYSICS LETTERS A 288.2(2001):88-94.
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