Fourier feature extraction combined with unsupervised clustering on total variations of amplitude, phase, and frequency classifies chimera state types in Rayleigh oscillator networks.
Controlling chimera states via minimal coupling modification.Chaos: an interdisciplinary journal of nonlinear science, 29(5)
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
nlin.PS 1years
2026 1verdicts
UNVERDICTED 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
Classification of Chimera States via Fourier Analysis and Unsupervised Learning
Fourier feature extraction combined with unsupervised clustering on total variations of amplitude, phase, and frequency classifies chimera state types in Rayleigh oscillator networks.