Analysis and test of efficient methods for building recursive deterministic perceptron neural networks

Neural Networks

Volumn 20, Issue 10, 2007, Pages 1095-1108

  Elizondo, David A ^a   Birkenhead, Ralph ^a   Góngora, Mario ^a   Taillard, Eric ^b   Luyima, Patrick ^a  

^a School of Computing   (United Kingdom)

^b UNIVERSITY OF APPLIED SCIENCES WESTERN SWITZERLAND   (Switzerland)

Author keywords

Batch learning; Convergence time; Generalisation; Incremental learning; Modular learning; Performance sensitivity analysis; Recursive deterministic perceptron; Topology

Indexed keywords

BACKPROPAGATION ALGORITHMS; CLASSIFICATION (OF INFORMATION); COMPUTATIONAL COMPLEXITY; PROBLEM SOLVING; RECURSIVE FUNCTIONS; TOPOLOGY;

CONVERGENCE TIME; INCREMENTAL LEARNING; MODULAR LEARNING; PERFORMANCE SENSITIVITY ANALYSIS; RECURSIVE DETERMINISTIC PERCEPTRON;

MULTILAYER NEURAL NETWORKS;

ARTICLE; ARTIFICIAL NEURAL NETWORK; GEOMETRY; LEARNING ALGORITHM; MATHEMATICAL PARAMETERS; PERCEPTRON; PRIORITY JOURNAL;

COMPUTER SIMULATION; HUMANS; LEARNING; NEURAL NETWORKS (COMPUTER); REPRODUCIBILITY OF RESULTS; RESEARCH DESIGN;

EID: 36549078208     PISSN: 08936080     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neunet.2007.07.009     Document Type: Article

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