A new recurrent neural network for solving convex quadratic programming problems with an application to the κ-winners-take-all problem

IEEE Transactions on Neural Networks

Volumn 20, Issue 4, 2009, Pages 654-664

  Hu, Xiaolin ^a   Zhang, Bo ^a  

^a TSINGHUA UNIVERSITY   (China)

Author keywords

Winners take all ( WTA); Asymptotic stability; Linear programming; Neural network; Quadratic programming

Indexed keywords

CONVEX QUADRATIC PROGRAMMING; GLOBAL CONVERGENCES; MATRIX INVERSE; MINI MAXES; MINIMAX PROBLEMS; NETWORK DESIGNS; NEURAL NETWORK FAMILIES; NUMERICAL SIMULATIONS; QUADRATIC PROGRAMMING PROBLEMS; SIMPLE STRUCTURES; STRUCTURAL COMPLEXITY; THEORETICAL RESULTS; VARIABLE SUBSTITUTIONS;

ASYMPTOTIC ANALYSIS; ASYMPTOTIC STABILITY; DYNAMIC PROGRAMMING; INVERSE PROBLEMS; LINEARIZATION; OPTIMIZATION; PARTICLE SIZE ANALYSIS; PROBLEM SOLVING; RECURRENT NEURAL NETWORKS;

COMPUTER SYSTEMS PROGRAMMING;

EID: 67349165051     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNN.2008.2011266     Document Type: Article

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