首页|Recent Findings in Machine Learning Described by Researchers from Polytechnic Un iversity Cartagena (Predicting Solar Cycle 26 Using the Polar Flux As a Precurso r, Spectral Analysis, and Machine Learning: Crossing a Gleissberg Minimum?)
Recent Findings in Machine Learning Described by Researchers from Polytechnic Un iversity Cartagena (Predicting Solar Cycle 26 Using the Polar Flux As a Precurso r, Spectral Analysis, and Machine Learning: Crossing a Gleissberg Minimum?)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting out of Cartagena, Spain, by N ewsRx editors, research stated, “This study introduces a novel method for predic ting the sunspot number (SN\documentclass[12pt]{minimal} \usep ackage{amsmath} \usepackage{ wasysym} \usepackage{amsfonts} \ usepackage{amssymb} \usepackage{ amsbsy} \usepackage {mathrsfs} \ usepackage{upgreek} \setlength{ \oddsidemargin}{-69pt} \ begin{document}$\ mathrm {S}_{\ mathrm{N}}$\ end{document}) of Solar Cycles 25 (the current cycle) and 26 using multivariate machine-learning techniques, the Sun’s polar flux as a precursor parameter, and the fast Fourier transform to conduct a spectral analy sis of the considered time series. Using the 13-month running average of the ver sion 2 of the SN\documentclass[12pt ]{minimal} \usepacka ge{amsmath} \usepackage{wa sysym} \ usepackage{amsfonts} \ usepackage{amssymb} \usepackage{ amsbsy} \usepackage{mathrsfs} \ usepackage{ upgreek} \setlength{ \oddsidemargin}{-69pt} \ begin{document}$\ mathrm{S}_{\ mathrm{N} }$\ end{document} provided by the World Data Center-SILSO, we are thu s able to present predictive results for the SN\docume ntclass[12pt]{minimal} \ usepackage{amsmath} \usepackage{ wasysym} \ usepackage{amsfonts} \ usepackage{amssymb} \usepackage{ amsbsy} \usepackage{mathrsfs} \ usepackage{ upgreek} \setlength{ \oddsidemargin}{-69pt} \ begin{document}$\ mathrm{S}_{\ mathrm{N} }$\ end{document} until January 2038, giving maximum peak values of 1 31.4 (in July 2024) and 121.2 (in September 2034) for Solar Cycles 25 and 26, re spectively, with a root mean square error of 10.0.”
CartagenaSpainEuropeCyborgsEmerg ing TechnologiesMachine LearningPolytechnic University Cartagena
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