Control-based method to identify underlying delays of a nonlinear dynamical system

Reconstruction of parameters and unobserved variables of a semiconductor laser with optical feedback from intensity time series

We propose a method for the reconstruction of time-delayed feedback systems having unobserved variables from scalar time series. The method is based on the modified initial condition approach, which allows one to significantly reduce the number of starting guesses for an unobserved variable with a time delay. The proposed method is applied to the reconstruction of the Lang-Kobayashi equations, which describe the dynamics of a single-mode semiconductor laser with external optical feedback. We consider the case where only the time series of laser intensity is observable and the other two variables of the model are hidden. The dependence of the quality of the system reconstruction on the accuracy of assignment of starting guesses for unobserved variables and unknown laser parameters is studied. The method could be used for testing the security of information transmission in laser-based chaotic communication systems.

Recovery of couplings and parameters of elements in networks of time-delay systems from time series

We propose a method for the recovery of coupling architecture and the parameters of elements in networks consisting of coupled oscillators described by delay-differential equations. For each oscillator in the network, we introduce an objective function characterizing the distance between the points of the reconstructed nonlinear function. The proposed method is based on the minimization of this objective function and the separation of the recovered coupling coefficients into significant and insignificant coefficients. The efficiency of the method is shown for chaotic time series generated by model equations of diffusively coupled time-delay systems and for experimental chaotic time series gained from coupled electronic oscillators with time-delayed feedback.

Reconstruction of ensembles of coupled time-delay systems from time series

We propose a method to recover from time series the parameters of coupled time-delay systems and the architecture of couplings between them. The method is based on a reconstruction of model delay-differential equations and estimation of statistical significance of couplings. It can be applied to networks composed of nonidentical nodes with an arbitrary number of unidirectional and bidirectional couplings. We test our method on chaotic and periodic time series produced by model equations of ensembles of diffusively coupled time-delay systems in the presence of noise, and apply it to experimental time series obtained from electronic oscillators with delayed feedback coupled by resistors.

Structure identification based on steady-state control: experimental results and applications

We report experimental results on structure identification of nonlinear systems by a steady-state control method. The idea underlying the method is to drive the nonlinear system to steady state by applying a suitable feedback control input. It turns out experimentally that this control-based structure identification method can be used for some applications, such as estimation of initial conditions and state variables of nonlinear systems and structure identification of some special elements. Two attractors of the Chua oscillator are presented to illustrate the reliability of the suggested techniques under the hypotheses of measurable state variables and physical access to the system for implementing the proportional feedback.

Reconstruction of time-delay systems using small impulsive disturbances

We propose a method for the reconstruction of time-delayed feedback systems from time series. The method is based on the analysis of the system response to a weak external disturbance having the form of rectangular pulses. To apply the method one must have access to the state variable of the system in order to perturb it and the time series of the driving signal and the system response having at least about one hundred points on the time interval equal to the delay time. The method is intended to recover delays in low-order time-delay systems performing periodic oscillations, but can also be applied to systems in chaotic regimes in the presence of high level of noise. We verify the method by applying it to both numerical and experimental data.