Automatic Solar Flare Detection Using MLP, RBF and SVM

Details Automatic Solar Flare Detection Using MLP, RBF and SVM Automatic Solar Flare Detection Using MLP, RBF and SVM

May 2003

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2003spd....34.0206q&link_type=abstract

American Astronomical Society, SPD meeting #34, #02.06; Bulletin of the American Astronomical Society, Vol. 35, p.808

Astronomy and Astrophysics

Astronomy

Scientific paper

The focus of the automatic solar flare detection is on the development of efficient feature-based classifiers. Three advanced techniques used in this work are Multi-Layer Perceptron (MLP), Radial Basis Function (RBF), and Support Vector Machine (SVM) classifiers. We have experimented and compared these three methods for solar flare detection on the solar Hα images obtained from Big Bear Solar Observatory in California. The preprocessing step is to obtain the nine principal features of the solar flares for the classifiers. Experimental results show that by using SVM, we can obtain the best classification rate of the solar flares. Our work would lead to real-time solar flare detection using advanced pattern recognition techniques.
The work is supported by National Science Foundation (NSF) under grants ATM 0076602 and ATM 0233931.

Affiliated with

Also associated with

No associations

Rating

Automatic Solar Flare Detection Using MLP, RBF and SVM 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 Automatic Solar Flare Detection Using MLP, RBF and SVM, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Automatic Solar Flare Detection Using MLP, RBF and SVM will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-985271