Replication of discrete chaos

2013-01-01
Akhmet, Marat
FEN, MEHMET ONUR
© 2019 Institute of Mathematical Statistic. All rights reserved.We provide extension of chaos by implementing chaotic perturbations to exponentially stable difference equations with arbitrarily high dimensions. Our analysis is based on the Li-Yorke definition of chaos. The results are supported with the aid of simulations.
6th International Conference on Chaotic Modeling and Simulation, CHAOS 2013

Suggestions

New criteria for generalized synchronization preserving the chaos type
Akhmet, Marat; FEN, MEHMET ONUR (2013-01-01)
© 2019 Institute of Mathematical Statistic. All rights reserved.We provide new conditions for the presence of generalized synchronization in unidirectionally coupled systems. One of the main results in the paper is the preservation of the chaos type of the drive system. The analysis is based on the Devaney definition of chaos. Appropriate simulations which illustrate the generalized synchronization are depicted.
Learning Narrowband Graph Spectral Kernels for Graph Signal Estimation Çizge Sinyallerinin Dar Bantli Spektral Kernel Öǧrenimi ile Kestirimi
Furkan Kar, Osman; Turhan, Gülce; Vural, Elif (2022-01-01)
In this work, we study the problem of estimating graph signals from incomplete observations. We propose a method that learns the spectrum of the graph signal collection at hand by fitting a set of narrowband graph kernels to the observed signal values. The unobserved graph signal values are then estimated using the sparse representations of the signals in the graph dictionary formed by the learnt kernels. Experimental results on graph data sets show that the proposed method compares favorably to baseline gr...
Estimation of Time-Varying Graph Signals by Learning Graph Dictionaries Zamanda Deǧişen Graf Sinyallerinin Kestirimi için Graflarda Sözlük Öǧrenme
Acar, Abdullah Burak; Vural, Elif (2022-01-01)
We study the problem of estimating time-varying graph signals from missing observations. We propose a method based on learning graph dictionaries specified by a set of time-vertex kernels in the joint spectral domain. The parameters of the time-vertex kernels are optimized jointly with the sparse representation coefficients of the signals, so that the learnt representation fits well to the available observations of the time-vertex signals at hand. The missing observations of the signals are then estimated b...
Seventh Graders' Statistical Literacy: an Investigation on Bar and Line Graphs
Çatman Aksoy, Emine; Işıksal Bostan, Mine (2020-02-10)
The aim of the current study was to investigate the statistical literacy of seventh grade students regarding the concepts of average and variation on bar and line graphs presented in alternative real-life contexts. To this end, the statistical literacy levels of the seventh grade students were initially determined. Subsequently, definitions, interpretations and evaluations generated by the students at varying levels of statistical literacy were examined in further depth. More specifically, a qualitative sur...
A stabilized FEM formulation with discontinuity-capturing for solving Burgers’-type equations at high Reynolds numbers
Cengizci, Süleyman; Uğur, Ömür (2023-04-01)
This computational study is concerned with the numerical solutions of Burgers’-type equations at high Reynolds numbers. The high Reynolds numbers drive the nonlinearity to play an essential role and the equations to become more convection-dominated, which causes the solutions obtained with the standard numerical methods to involve spurious oscillations. To overcome this challenge, the Galerkin finite element formulation is stabilized by using the streamline-upwind/Petrov–Galerkin method. The stabilized form...
Citation Formats
M. Akhmet and M. O. FEN, “Replication of discrete chaos,” presented at the 6th International Conference on Chaotic Modeling and Simulation, CHAOS 2013, İstanbul, Türkiye, 2013, Accessed: 00, 2022. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85072326752&origin=inward.