Next Article in Journal
A New Validity Index Based on Fuzzy Energy and Fuzzy Entropy Measures in Fuzzy Clustering Problems
Previous Article in Journal
FASTENER Feature Selection for Inference from Earth Observation Data
Previous Article in Special Issue
Optimization for Software Implementation of Fractional Calculus Numerical Methods in an Embedded System
Open AccessArticle

Quantifying Information without Entropy: Identifying Intermittent Disturbances in Dynamical Systems

1
Sandia National Laboratories, Albuquerque, NM 87185, USA
2
William E. Boeing Department of Aeronautics & Astronautics, University of Washington, Seattle, WA 98195, USA
3
Department of Civil, Construction, & Environmental Engineering, University of New Mexico, Albuquerque, NM 87131, USA
*
Author to whom correspondence should be addressed.
Entropy 2020, 22(11), 1199; https://doi.org/10.3390/e22111199
Received: 23 September 2020 / Revised: 18 October 2020 / Accepted: 19 October 2020 / Published: 23 October 2020
(This article belongs to the Special Issue Entropy in Dynamic Systems II)
A system’s response to disturbances in an internal or external driving signal can be characterized as performing an implicit computation, where the dynamics of the system are a manifestation of its new state holding some memory about those disturbances. Identifying small disturbances in the response signal requires detailed information about the dynamics of the inputs, which can be challenging. This paper presents a new method called the Information Impulse Function (IIF) for detecting and time-localizing small disturbances in system response data. The novelty of IIF is its ability to measure relative information content without using Boltzmann’s equation by modeling signal transmission as a series of dissipative steps. Since a detailed expression of the informational structure in the signal is achieved with IIF, it is ideal for detecting disturbances in the response signal, i.e., the system dynamics. Those findings are based on numerical studies of the topological structure of the dynamics of a nonlinear system due to perturbated driving signals. The IIF is compared to both the Permutation entropy and Shannon entropy to demonstrate its entropy-like relationship with system state and its degree of sensitivity to perturbations in a driving signal. View Full-Text
Keywords: information entropy; discontinuity detection; intermittent disturbance; nonlinear dynamical system information entropy; discontinuity detection; intermittent disturbance; nonlinear dynamical system
Show Figures

Figure 1

MDPI and ACS Style

Montoya, A.; Habtour, E.; Moreu, F. Quantifying Information without Entropy: Identifying Intermittent Disturbances in Dynamical Systems. Entropy 2020, 22, 1199. https://doi.org/10.3390/e22111199

AMA Style

Montoya A, Habtour E, Moreu F. Quantifying Information without Entropy: Identifying Intermittent Disturbances in Dynamical Systems. Entropy. 2020; 22(11):1199. https://doi.org/10.3390/e22111199

Chicago/Turabian Style

Montoya, Angela; Habtour, Ed; Moreu, Fernando. 2020. "Quantifying Information without Entropy: Identifying Intermittent Disturbances in Dynamical Systems" Entropy 22, no. 11: 1199. https://doi.org/10.3390/e22111199

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop