A novel approach for estimation of optimal embedding parameters of nonlinear time series by structural learning of neural network

Neurocomputing

Volumn 70, Issue 7-9, 2007, Pages 1360-1371

  Manabe, Yusuke ^b   Chakraborty, Basabi ^a  

^a IWATE PREFECTURAL UNIVERSITY   (Japan)

^b NONE

Author keywords

Chaos; Embedding parameters; Embedding theorem; Neural network; Nonlinear time series; Strange attractor; Structural learning

Indexed keywords

CHAOS THEORY; COMPUTER SIMULATION; LEARNING SYSTEMS; NONLINEAR SYSTEMS; TIME SERIES ANALYSIS;

EMBEDDING THEOREM; NONLINEAR TIME SERIES; STRUCTURAL LEARNING;

FEEDFORWARD NEURAL NETWORKS;

ACCURACY; ANALYTICAL ERROR; ANALYTICAL PARAMETERS; ARTICLE; ARTIFICIAL NEURAL NETWORK; BAYES THEOREM; COMPUTER PROGRAM; CONTROLLED STUDY; INTERMETHOD COMPARISON; LEARNING; MATHEMATICAL COMPUTING; MEASLES; NONLINEAR SYSTEM; PREDICTION; PRIORITY JOURNAL; PROBABILITY; PROCESS OPTIMIZATION; SIGNAL NOISE RATIO; SIMULATION; TIME SERIES ANALYSIS; VALIDATION PROCESS;

EID: 33847395391     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2006.06.005     Document Type: Article

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