Design of general projection neural networks for solving monotone linear variational inequalities and linear and quadratic optimization problems

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Volumn 37, Issue 5, 2007, Pages 1414-1421

  Hu, Xiaolin ^a   Wang, Jun ^a  

^a CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

Global convergence; Linear programming; Linear variational inequality (LVI); Quadratic programming; Recurrent neural network

Indexed keywords

CONVERGENCE OF NUMERICAL METHODS; LINEAR PROGRAMMING; OPTIMIZATION; PROBLEM SOLVING; QUADRATIC PROGRAMMING;

GENERAL PROJECTION NEURAL NETWORKS; GLOBAL CONVERGENCE; LINEAR VARIATIONAL INEQUALITY (LVI); QUADRATIC OPTIMIZATION;

RECURRENT NEURAL NETWORKS;

ALGORITHM; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; AUTOMATED PATTERN RECOGNITION; COMPUTER SIMULATION; LETTER; METHODOLOGY; STATISTICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; LINEAR MODELS; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, AUTOMATED;

EID: 35148879483     PISSN: 10834419     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSMCB.2007.903706     Document Type: Article

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