A genetic-based neuro-fuzzy approach for prediction of solar activity

Details A genetic-based neuro-fuzzy approach for prediction of solar activity A genetic-based neuro-fuzzy approach for prediction of solar activity

Sep 2004

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2004spie.5497..542a&link_type=abstract

Modeling and Systems Engineering for Astronomy. Edited by Craig, Simon C.; Cullum, Martin J. Proceedings of the SPIE, Volume 549

Astronomy and Astrophysics

Astronomy

Scientific paper

This paper presents an application of the neuro-fuzzy modeling to analyze the time series of solar activity, as measured through the relative Wolf number. The neuro-fuzzy structure will be optimized based on the linear adapted genetic algorithm with controlling population size (LAGA-POP). First, the dimension of the time series characteristic attractor is obtained based on the smallest Regularity Criterion (RC) and the neuro-fuzzy modeling. Second, after describing the neuro-fuzzy structure and optimizing its parameters based on LAGA-POP, the performance of the present approach in forecasting yearly sunspot numbers is favorably compared to that of other published methods. Finally, the comparison predictions for the remaining part of the 22nd and the whole 23rd cycle of solar activity are presented.

Affiliated with

Also associated with

No associations

Rating

A genetic-based neuro-fuzzy approach for prediction of solar activity 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 A genetic-based neuro-fuzzy approach for prediction of solar activity, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and A genetic-based neuro-fuzzy approach for prediction of solar activity will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-1822883