Data analysis using a combination of independent component analysis and empirical mode decomposition

Details Data analysis using a combination of independent component analysis and empirical mode decomposition Data analysis using a combination of independent component analysis and empirical mode decomposition

Jun 2009

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2009phrve..79f6705l&link_type=abstract

Physical Review E, vol. 79, Issue 6, id. 066705

Mathematics

Logic

4

Neural Networks, Fuzzy Logic, Artificial Intelligence, Time Series Analysis, Time Variability

Scientific paper

A combination of independent component analysis and empirical mode decomposition (ICA-EMD) is proposed in this paper to analyze low signal-to-noise ratio data. The advantages of ICA-EMD combination are these: ICA needs few sensory clues to separate the original source from unwanted noise and EMD can effectively separate the data into its constituting parts. The case studies reported here involve original sources contaminated by white Gaussian noise. The simulation results show that the ICA-EMD combination is an effective data analysis tool.

Affiliated with

Also associated with

No associations

Rating

Data analysis using a combination of independent component analysis and empirical mode decomposition does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Data analysis using a combination of independent component analysis and empirical mode decomposition, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Data analysis using a combination of independent component analysis and empirical mode decomposition will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-1644658