2,264 research outputs found
Analyzing communication schemes using methods from nonlinear filtering
The perfomance of a class of communication systems is investigated in a probabilistic framework. We investigate the bit error probability of the optimal as well as approximately optimal receivers. In general the latter turn out to be unavoidable due to the computational complexity of the former. (C) 2003 American Institute of Physics
Inferring local dynamics and connectivity of spatially extended systems with long-range links based on steady-state stabilization
A method is presented for system identification of spatially extended systems with structural inhomogeneities of local dynamics and additional long-range links. The proposed identification procedure is based on steady-state stabilization and is illustrated with an inhomogeneous two-dimensional grid of coupled FitzHugh-Nagumo models
Efficient noncausal noise reduction for deterministic time series
We present a simple noncausal noise reduction algorithm for time series that consist of noisy measurements of the state vectors of a deterministic (chaotic) nonlinear system. The underlying dynamical system is assumed to be known and to operate in discrete time. The noise reduction algorithm is an iterative scheme for finding exact deterministic orbits close to the measured noisy orbits. Furthermore, we discuss cases where the solution is not the original orbit but homoclinic to it. (C) 2001 American Institute of Physics
Synchronization and antisynchronization of chaotic power drop-outs and jump-ups of coupled semiconductor lasers
Experimental observations and numerical simulations of synchronization and antisynchronization of low-frequency power drop-outs and jump-ups of chaotic semiconductor lasers are presented
Laser stabilization with multiple-delay feedback control
Stabilization of chaotic intensity fluctuations of intracavity frequency-doubled solid-state (Nd: YAG) lasers using multiple-delay feedback control (MDFC) is demonstrated by numerical simulations. It is shown that MDFC not only provides stable (cw) output for constant pump rates but also works with slowly varying pump currents, resulting in corresponding (nonchaotic) intensity modulations. (c) 2006 Optical Society of America
Hybrid systems forming strange billiards
Hybrid dynamical systems consist of piecewise defined continuous time evolution processes interfaced with some logical or decision making process. These switches between different evolutions are triggered if the continuous state of the system reaches thresholds in state space. In the present work we investigate hybrid systems forming a special type of dynamical systems, so-called strange billiards. They show a rich variety of dynamical behavior including some unusual bifurcations and chaos, even if the continuous part of the system evolution is just linear. By means of Poincare map techniques we discuss different dynamical behaviors. Applications to the simulation of manufacturing systems and consequences for their dynamical behavior are outlined
Modeling chaotic and spatially extended systems
Different aspects of local modeling of chaotic time series are discussed including cross validation methods and algorithms for fast nearest neighbor search. The resulting local models are used for predicting the temporal evolution of low-dimensional and spatio-temporal systems
Prediction of spatiotemporal time series based on reconstructed local states
Spatiotemporal time series are analyzed and predicted using reconstructed local states. As numerical examples the evolution of a Kuramoto-Sivashinsky equation and a coupled map lattice are predicted from previously sampled data
Robust method for experimental bifurcation analysis
We present a robust method to locate and continue period-doubling, saddle-node and symmetry-breaking bifurcations of periodically driven experimental systems. The method is illustrated from results obtained for an electronic implementation of a Duffing oscillator
Modeling parameter dependence from time series
Two approaches for modeling of parameter dependence of dynamical systems from time series are investigated and applied to different examples. For both methods it is assumed that a few Lime series are available that have been measured for different (known) parameter values of the underlying (experimental) dynamical System. The objective is to model the changing dynamics of the system as a function of its parameters and to use this for experimental bifurcation analysis. Using parametrized families the tasks of modeling the dynamics and of modeling its parameter dependence are separated. Technical difficulties that may occur with this approach are discussed and illustrated. An alternative are extended state space models where both modeling, tasks are treated simultaneously. To obtain reliable models from a few time series only, ensembles of models are employed that show very good extrapolation and generalization properties
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