A decomposition principle for complexity reduction of artificial neural networks

Neural Networks

Volumn 9, Issue 6, 1996, Pages 999-1016

  Zong Ben, Xu ^a   Chung Ping, Kwong ^b  

^a XI'AN JIAOTONG UNIVERSITY   (China)

^b Xi'an Jiaotong University ^*

Author keywords

associative memories; best approximation projection; decomposition principle; elementary matrix transformation; Hopfield type networks; interpolation operator

Indexed keywords

ASSOCIATIVE STORAGE; COMPUTER SIMULATION; INTERPOLATION; LEARNING ALGORITHMS; PATTERN RECOGNITION; THEOREM PROVING; VECTORS;

COMPLEXITY REDUCTION; DECOMPOSITION ALGORITHMS; DECOMPOSITION PRINCIPLE; HOPFIELD TYPE ASSOCIATIVE MEMORY NETWORKS; NETWORK'S CONNECTION COMPLEXITY;

NEURAL NETWORKS;

ARTICLE; ARTIFICIAL NEURAL NETWORK; LEARNING; MATHEMATICAL ANALYSIS; PATTERN RECOGNITION; PRIORITY JOURNAL;

EID: 0030220519     PISSN: 08936080     EISSN: None     Source Type: Journal    
DOI: 10.1016/0893-6080(95)00055-0     Document Type: Article

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