A one-layer dual recurrent neural network with a heaviside step activation function for linear programming with its linear assignment application

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6792 LNCS, Issue PART 2, 2011, Pages 253-260

  Liu, Qingshan ^a   Wang, Jun ^b  

^a SOUTHEAST UNIVERSITY   (China)

^b CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

global convergence in finite time; linear assignment problem; linear programming; Recurrent neural networks

Indexed keywords

ACTIVATION FUNCTIONS; DECISION VARIABLES; FINITE TIME; FINITE-TIME CONVERGENCE; GAIN PARAMETER; GLOBAL CONVERGENCE; GLOBALLY CONVERGENT; HEAVISIDE; LINEAR ASSIGNMENT; LINEAR ASSIGNMENT PROBLEM; LINEAR PROGRAMMING PROBLEM; LOWER BOUNDS; MODEL COMPLEXITY; OPTIMAL SOLUTIONS; OPTIMIZATION PROBLEMS; SALIENT FEATURES; SIMULATION RESULT;

COMPUTER SIMULATION; COMPUTER SYSTEMS PROGRAMMING; LINEAR PROGRAMMING; NETWORK LAYERS; OPTIMIZATION;

RECURRENT NEURAL NETWORKS;

EID: 79959349867     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-21738-8_33     Document Type: Conference Paper

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