2 edition of **Chaotic evolution and nonlinear prediction in signal separation applications** found in the catalog.

Chaotic evolution and nonlinear prediction in signal separation applications

William W. Taylor

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- 0 Currently reading

Published
**1992**
by Rand in Santa Monica, CA
.

Written in English

- Ergodic theory.,
- Topological dynamics.

**Edition Notes**

Statement | William W. Taylor. |

The Physical Object | |
---|---|

Pagination | 18 p. : |

Number of Pages | 18 |

ID Numbers | |

Open Library | OL16593033M |

Predicting Rare Events in Multiscale Dynamical Systems Using Machine Learning Soon Hoe Lim,1, Ludovico Theo Giorgini,1 Woosok Moon,2 and J. S. Wettlaufer1,3 1Nordita, Royal Institute of Technology and Stockholm University, Stockholm 91, Sweden 2Department of Mathematics, Stockholm University, 91 Stockholm, Sweden 3Yale University, New Haven, Connecticut , USACited by: 1. 1. Introduction. Epilepsy is considered a window to the brain’s function and is therefore an increasingly active, interdisciplinary field of research [1–2].The “sacred” or “divine” disease is among the most common disorders of the nervous system, second only to stroke and Alzheimer’s disease, and affects 1–2% of the world’s population [3, 4].Cited by:

Chelidze, D., Cusumano, J. P., and Chatterjee, A., , “Failure prognosis using nonlinear short-time prediction and multi-time scale recursive estimation,” Proceedings of the ASME Design Engineering Technical Conference: 18th Biennial Conference on Mechanical Vibration and Noise, 6 A, Sep 9–12 , Pittsburgh, PA, pp. – Exploiting the time-reversal invariance and reciprocal properties of the lossless wave equation enables elegantly simple solutions to complex wave-scattering problems, and is embodied in the time-reversal mirror. A time-reversal mirror operates by recording the wave disturbance as a function of time at one or more points in a medium, broadcasting a time-reversed version of the signal(s) in the Cited by:

EEG signal which measures the electrical activity of a brain, EMG signal represents the electrical activity generated from muscle fibers and also finger pulse signal measures the blood flow in our body. Biological signals are decidedly nonlinear [1] and usually exhibit complex behavior with . Nonlinear Time Reversal in a Wave Chaotic System Matthew Frazier,1 Biniyam Taddese,1,2 Thomas Antonsen,1,2 and Steven M. Anlage1,2 1Department of Physics, University of Maryland, College Park, Maryland , USA 2Department of Electrical and Computer Engineering, University of Maryland, College Park, Maryland , USA (Received 2 July ; revised manuscript .

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Additional Physical Format: Online version: Taylor, William W. Chaotic evolution and nonlinear prediction in signal separation applications.

Santa Monica, CA: Rand, Identification of linear systems driven by chaotic signals using nonlinear prediction Article in IEEE Transactions on Circuits and Systems I Fundamental Theory and Applications 49(2) - A chaotic modular found for realizing the prediction of signal control was applied to three different physical models: the Lorenz attractor, Chua's circuit, and chaotic wake flow.

Abstract. This chapter deals with time series analysis for observations taken on systems that exhibit aperiodic or chaotic behavior. Such systems, which must be nonlinear, abound in physical and biological applications, and have been studied for nearly a century (Poincaré, ) in contexts ranging from classical mechanics and fluid dynamics to by: 2.

Complex and Chaotic Nonlinear Dynamics Advances in Economics and Finance, Mathematics and Statistics. including for instance the evolution of macroeconomic growth models towards nonlinear structures as well as signal processing applications to stock markets, fundamental parts of the book are devoted to the use of nonlinear dynamics in.

In Section 3, the general problem of nonlinear prediction is explained, and a discussion concerning the choice of suitable embedding parameters for a nonlinear predictor of a chaotic signal is given. In Section 4 the problem of chaotic signal prediction is considered. The intended purpose of this section is two-fold: to demonstrate that the Cited by: Anke Meyer-Baese, Volker Schmid, in Pattern Recognition and Signal Analysis in Medical Imaging (Second Edition), Introduction.

Neural networks are excellent candidates for feature extraction and selection, and for signal underlying architectures are mostly employing unsupervised learning algorithms and are viewed as nonlinear dynamical systems.

Time series modeling and prediction has been an active area of research due to the wide variety of applications in the ﬁnancial market, weather, biology, etc. The initial ap-proaches typically relied on AR, MA, or ARMA univariate models.

More sophisticated approaches rely on nonlinear modeling [6] and state space projection of the time series. The approximate Prediction-Based Control method (aPBC) is the continuous-time version of the well-known Prediction-Based Chaos Control method applied to stabilize periodic orbits of nonlinear dynamical systems.

The method is based on estimating future states of the free system response of continuous-time systems using the solution from the Runge-Kutta implicit method in real : Thiago P. Chagas, Pierre-Alexandre Bliman, Pierre-Alexandre Bliman, Karl Heinz Kienitz. This book starts with a discussion of nonlinear ordinary differential equations, bifurcation theory and Hamiltonian dynamics.

It then embarks on a systematic discussion of the traditional topics of modern nonlinear dynamics -- integrable systems, Poincaré maps, chaos, fractals and strange by: 6. Chaotic Dynamics of Nonlinear Systems (Dover Books on Physics) Reprint Edition applications, theory, and technique.

Suitable for advanced undergraduates and graduate students, researchers, and teachers of mathematics, physics, and engineering, the text's major prerequisite is familiarity with differential equations and linear vector spaces.

Cited by: When graphing a linear system as we have above, we are marking the whole infinity of results across the entire graphed range.

Pick any point on the x-axis, it need not be a whole number, draw a vertically until it intersects the graphed line, the y-axis value at that exact point is the solution to the formula for the x-axis value.

We know, and can see, that 2 * 2 = 4 by this method. CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): We use concepts from chaos theory in order to model nonlinear dynamical systems that exhibit deterministic behavior.

Observed time series from such a system can be embedded into a higher dimensional phase space without the knowledge of an exact model of the underlying dynamics.

CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): A novel method for regression has been recently proposed by V. Vapnik et al. [8, 9]. The technique, called Support Vector Machine (SVM), is very well founded from the mathematical point of view and seems to provide a new insight in function approximation.

We implemented the SVM and tested it on the same data base of. Nonlinear time series analysis completely new concepts and algorithms for time series analysis which can lead to a thorough understanding of the signal.

The book introduces a broad choice of such concepts and methods, including phase space embeddings, nonlinear prediction and noise reduction, Lyapunov exponents, dimensions and entropies, as.

An introduction to the study of chaotic systems via numerical analysis, this work includes many applications in physics and employs differential equations, linear vector spaces and some Hamiltonian systems.

Chaotic Dynamics of Nonlinear Systems S. Neil Rasband No preview available - Common terms and phrases. Chaos theory is a branch of mathematics focusing on the study of chaos—states of dynamical systems whose apparently-random states of disorder and irregularities are often governed by deterministic laws that are highly sensitive to initial conditions.

Chaos theory is an interdisciplinary theory stating that, within the apparent randomness of chaotic complex systems, there are underlying. Nonlinear chaotic model for predicting storm surges the fact that the waves themselves were highly nonlinear.

In real applications, however, the water level dynamics modeling, nonlinear dynamics and chaos theory, chaotic model prediction, case study, model results and conclusion.

forecast by the nonlinear prediction method can be - satisfactory. Time series of the dynamical variables are however not always chaotic, and chaotic behaviour must be examined for each time series.

In series of papers it has been developed an effective version of using a chaos theory method and non-linear prediction approach to studying chaotic. high signal-to-noise ratios. For tasks with multichannel input and/or output, the ESN approach can be accommo-dated simply by adding more input or output neurons (16, 18).

ESNs can be applied to all basic tasks of signal processing and control, including time series prediction, inverse modeling, pattern generation, event detection and.

and inexpensive. Many researchers have investigated the implications of chaotic signals in communication systems [8][9][10][11][12]. This paper focuses on build, modified, and analyzed chaotic oscillator based Chua circuit, synchronization two identical chaotic attractor systems and its applications in signal masking by: 4.Not all nonlinear systems are chaotic.

However a chaotic system is necessarily nonlinear. There doesn't exists a definition for chaos but using the one given by Strogatz, ref 1: Chaos is aperiodic long-termed behavior in a deterministic system that exhibits sensitive dependence on. Chaotic systems can be very simple, but they produce signals of surprising complexity.

One characteristic of a chaotic system is that the signals produced by a chaotic system do not synchronize with any other system.

It therefore seems impossible for two chaotic systems to synchronize with each other, but if the two systems exchange information in just the right way, they can by: