Predicting equation from Chaotic data by Nonlinear Singular Value Decomposition
Prabhakar G. Vaidya, P. S. Sajini Anand
Abstract
Singular Value Decomposition can be considered as an effective method for Signal Processing/especially data compression. In this short paper we investigate the application of SVD to predict data equation from data. The method is similar to nonlinear ARMA method for fitting a nonlinear equation to the data.
Create a lesson
Related papers
Exploring continuous beta-ensembles: A Python implementation for random matrix spectral statistics
Dorin Weissman
Mutual information-entropy plane: a new quantifier space for time series analysis
Gonzalez Acosta Gaspar, Kowalski Andrés M
Characterization of Chaotic Evolution in Quantum Systems Induced by Random Hermitian Matrices
Arkady Kurnosov, Sven Gnutzmann, Uzy Smilansky
Noise Effects on Ordinal Pattern Statistics via Majorization
Facundo Sapienza
Synchronization induces Bell violations in a model of walking droplets
Álvaro G. López, Rahil N. Valani, Yuanmei Li et al.
Identifying the structure of dynamical transitions in logistic map
Aswin Balaji, Shruti Tandon, Shwetha Viswesh et al.