Detecting Noise in Chaotic Signals through Principal Component Matrix Transformation
نویسندگان
چکیده
We study the reconstruction of continuous chaotic attractors from noisy time-series. A method of delays and principal component eigenbasis defined by singular vectors is used for state vectors reconstruction. We introduce a simple measure of trajectory vectors directional distribution for chosen principal component subspace, based on nonlinear transformation of principal component matrix. The value of such defined measure is dependent on the amount of noise in the data. For isotropically distributed noise or close to isotropic , that allows us to set up window width boundaries for acceptable attractor reconstruction as a function of noise content in the data.
منابع مشابه
A Critique on Power Spectrum – Area Fractal Method for Geochemical Anomaly Mapping
Power spectrum – area fractal (S-A fractal) method has been frequently applied for geochemical anomaly mapping. Some researchers have performed this method for separation of geochemical anomaly, background and noise and have delineated their distribution maps. In this research, surface geochemical data of Zafarghand Cu-Mo mineralization area have been utilized and some defects of S-A fractal me...
متن کاملDetecting Huntington Patient Using Chaotic Features of Gait Time Series
Huntington's disease (HD) is a congenital, progressive, neurodegenerative disorder characterized by cognitive, motor, and psychological disorders. Clinical diagnosis of HD relies on the manifestation of movement abnormalities. In this study, we introduce a mathematical method for HD detection using step spacing. We used 16 walking signals as control and 20 walking signals as HD. We took a s...
متن کاملSeparation Between Anomalous Targets and Background Based on the Decomposition of Reduced Dimension Hyperspectral Image
The application of anomaly detection has been given a special place among the different processings of hyperspectral images. Nowadays, many of the methods only use background information to detect between anomaly pixels and background. Due to noise and the presence of anomaly pixels in the background, the assumption of the specific statistical distribution of the background, as well as the co...
متن کاملA Novel Method for Detection of Epilepsy in Short and Noisy EEG Signals Using Ordinal Pattern Analysis
Introduction: In this paper, a novel complexity measure is proposed to detect dynamical changes in nonlinear systems using ordinal pattern analysis of time series data taken from the system. Epilepsy is considered as a dynamical change in nonlinear and complex brain system. The ability of the proposed measure for characterizing the normal and epileptic EEG signals when the signal is short or is...
متن کاملNonlinear Detection of Weak Pseudoperiodic Chaotic Signal Frequencies from Noisy Environment
The task of signal detection and separation is a central theme in a wide variety of fields. Many techniques exist to improve the capability of detecting or enhancement a weak target signal corrupted by the additive Gaussian noise or the background noise generated by an unknown nonlinear dynamical mechanism. Due to its simplicity in implementation and efficiency in computation, noise reduction b...
متن کامل