@article{Sajjad2026, 
author = {Haseeba Sajjad and Adil Jhangeer and Mudassar Imran and Ali R Ansari},
title = {Robust dynamical behavior identification in the Rabinovich Fabrikant system using statistical measures},
year = {2026},
journal = {AIMS Mathematics},
volume = {11},
number = {4},
pages = {10716-10743},
keywords = {nonlinear dynamical systems, statistical analysis, power spectral density, kernel density estimation, empirical cumulative distribution function, ensemble sensitivity, parametric space analysis},
url = {https://www.sciopen.com/article/10.3934/math.2026441},
doi = {10.3934/math.2026441},
abstract = {This study presents a dynamical and statistical investigation of the Rabinovich Fabrikant system within the bounded control parameter space    (  p  ,  q  )  ∈  [  −  1  ,  1  ]  ×  [  −  1  ,  1  ], using ensembles of initial conditions sampled from    [  −  0.1  ,  0.1  ]. A systematic grid-based exploration of the parameter space revealed clearly distinguishable regions corresponding to stable equilibria, sustained periodic oscillations, and unstable dynamical regimes. A grid-based systematic scan of the parameter space indicated that the space is evidently divided into easily recognizable domains that are characterized by a stable equilibrium, periodic oscillations, and unstable dynamical states. The analysis of time series and power spectral density was used to describe the time dynamics of system responses and to identify a change between steady, oscillatory, and broadband states of the system. In addition to these classical dynamical diagnostics, an ensemble-based statistical analysis was carried out to measure the variability of the system dynamics. The probability distributions of the dynamical states were measured by kernel density estimation, which demonstrated that stable and periodic regimes have convergent and sharp distributions, and transitional regions have wider distributions and slower convergence. Moreover, empirical cumulative distribution functions will gave more information about the structure and variability of the distribution of the system responses in various parameter regimes. Sensitivity analysis also implied that the impact of initial conditions is also highly parameter sensitive and is especially pronounced at regime boundaries. These findings offer a quantitative and reproducible parameter space mapping of the dynamics of RF systems and make ensemble-based statistical diagnostics fundamental instruments to measuring robustness and uncertainty in nonlinear chaotic systems.}
}