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Monday, August 10, 2020 | History

2 edition of Smoothing chaotic signals for system identification found in the catalog.

Smoothing chaotic signals for system identification

D. Coca

Smoothing chaotic signals for system identification

a multiresolution wavelet decomposition approach

by D. Coca

  • 279 Want to read
  • 31 Currently reading

Published by University of Sheffield, Department of Automatic Control and Systems Engineering in Sheffield .
Written in English


Edition Notes

StatementD.Coca and S.A. Billings.
SeriesResearch report / University of Sheffield, Dept.of Automatic Control and Systems Engineering -- no.587, Research report (University of Sheffield Department of Automatic Control and Systems Engineering) -- no 587.
ContributionsBillings, S. A.
ID Numbers
Open LibraryOL17272467M

Download Signals Systems Books (Electromagnetics Books) – We have compiled a list of Best & Standard Reference Books on Signals Systems (Electromagnetics) Subject for Electrical Engineering & Electronics and Communication Engineering Students & for books are used by many students & graduates of top universities, institutes and colleges.   Chaotic signals are characterized by irregularity, aperiodicity, decorrelation, and broadband. They can be generated through simple deterministic dynamical systems [] and have promising applications in cryptography [], random number generation [3, 4], watermarking [], communication [6, 7], and systems modeling [].The well-known properties of decorrelation and broadband of chaotic signals .

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS—I: FUNDAMENTAL THEORY AND APPLICATIONS, VOL. 44, NO. 5, MAY A New Approach to Communications Using Chaotic Signals Ned J. Corron, Member, IEEE, and Daniel W. Hahs Abstract— In this paper, a new approach for communication using chaotic signals is presented. In this approach, the trans-. The problem of adaptive secure communication based on chaotic parameter modulation is discussed. Information signals are modulated to some parameter of the chaotic system in the master system, then, adaptive parameter identification and single controller is designed to structure an adaptive response system.

system state and fast detection of chaotic patterns. In such cases, a signal processing approach with time-frequency methods may be an appropriate choice. For example, the wavelet transform and the scalogram have been extensively used [1] to study turbulent signals. The analysis of ridges in the time-scale or time-frequency plane allows to. whether or not a chaotic system has produced the signals un-der consideration is not crucial for being able to perform the classification task. 2. Classification Problem For simplicity, we consider the case where two classes of approximately periodic signals are given and we have to find an algorithm that decides to which class a given.


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Smoothing chaotic signals for system identification by D. Coca Download PDF EPUB FB2

System Identification Of Nonlinear Chaotic Signals: Using Neural Network Approach [Dhoble, Sagar] on *FREE* shipping on qualifying offers. System Identification Of Nonlinear Chaotic Signals: Using Neural Network Approach.

Abstract. A new wavelet based smoothing algorithm is introduced to reduce the noise affecting chaotic signals prior to system identification. The algorithm involves a miltiresolution decomposition of the signal using B-spline wavelets which makes use of the mutual information between neighbouring points in Author: D.

Coca and S.A. Billings. Chaotic signals generated in nonlinear electrical circuits [7–9] and lasers [10] can potentially be used as carriers for information transmission in a communication system. The advantage of a broadband information carrier is that it can enhance the robustness of communication channels to interferences with narrow-band disturbances.

The blind identification of linear convolution systems driven by a piecewise linear continuous chaotic signal was researched, and a chaos synchronization based identification approach for linear.

System identification is an important signal processing operation. In this paper, we consider the problem of parameter estimation for a finite impulse response (FIR) system driven by chaotic. Abstract. In this chapter, the problems Smoothing chaotic signals for system identification book identification, modeling, and forecasting of chaotic signals are discussed.

These problems are solved with the use of the conventional techniques of computational intelligence as radial basis neural networks and learning neuro-fuzzy architectures, as well as novel hybrid structures based on the Kolmogorov’s superposition theorem and using the neo. Recent studies show that the presence of noise in chaotic signals drastically diminishes the performance of many techniques such as system identification, parameter estimation, and prediction.

Thus, it can be inferred that preprocessing of chaotic signals, so as to reduce the noise without damaging the main underlying dynamic of the signal. In this article the identification of AR system driven by chaotic sequences is addressed.

This problem emerges in chaotic communication system, in which chaos-modulated signal passes through a. Chaotic Signals in Digital Communications combines fundamental background knowledge with state-of-the-art methods for using chaotic signals and systems in digital communications.

The book builds a bridge between theoretical works and practical implementation to help researchers attain consistent performance in realistic environments. This chapter introduces nonlinear dynamical systems known as chaotic systems and describes their suitability for application to secure communications.

A nonlinear or chaotic signal is characterised by its high sensitivity to parameter and initial condition perturbations. System identification usually is the first step to understand control system and to design controllers, especially where performance matters, such as high speed and high precision motion control system.

Theoretically, there are certain conditions of the excitation signals. For example, the white noise is one such type of signal. However, some modes are not allowed to be excited in practice. To show the performance of the proposed control strategy we carried out a second simulation using the same set-up as above, and fixing the receiver system gains as k 1 = k 2 = and m = 4; with the receiver system initialized at w 2 (0) = 0, p ˆ (0) = 0, s ˆ 1 =-2 and s ˆ 2 =In Fig.

2 we can see that the synchronization errors asymptotically converge to zero. In other words, the dither smoothing technique enables the state feedback linearization method to be applied to chaotic systems involving undifferentiable nonlinearities.

This study presents the application of the dither smoothing technique to a chaotic circuit in order to demonstrate the proposed strategy. Chaotic system identification based on Kalman filter smoothing and interpolation in the usual way.

of shadowing from dynamical systems theory. 1 1 Introduction In the work that has been. electronics Article Accurate Synchronization of Digital and Analog Chaotic Systems by Parameters Re-Identification Timur Karimov 1,* ID, Denis Butusov 2, Valery Andreev 1, Artur Karimov 2 and Aleksandra Tutueva 1 1 Department of Computer-Aided Design, Saint-Petersburg Electrotechnical University “LETI”, Saint PetersburgRussia; [email protected] (V.A.); [email protected] (A.T.).

The book fills a gap in the existing literature where a number of books exist that deal with chaos and chaotic communications but not with synchronization of chaotic communication systems.

It also acts as a bridge between communication system theory and chaotic synchronization by carefully explaining the two concepts and demonstrating how they Reviews: 1. We refine here this approach by deriving a fully consistent SSA-MEM spectral estimate.

Vautard et al. SSA: A toolkit for noisy chaotic signals c. c - IPCC I IPCC IPCC 1- 8 IPCC IPCC raw Time Fig. Reconstructed subsets R,-0x of the IPCC series with ending date in only. Signals and Systems: A First Look System Classifications and Properties Introduction In this module some of the basic classifications of systems will be briefly introduced and the most important properties of these systems are explained.

As can be seen, the properties of. The vulnerability of chaotic communication systems to noise in transmission channel is a serious obstacle for practical applications. Traditional signal processing techniques provide only limited. The book also presents a detailed literature review on the topic of synchronization of chaotic communication systems.

Furthermore, it presents the literature review on the general topic of chaotic synchronization and how those ideas led to the application of chaotic signals to secure chaotic communication systems.

ISBN: OCLC Number: Description: ix, pages: illustrations ; 26 cm. Contents: An overview of chaotic signal processing --Target recognition using nonlinear dynamics --Communicating with exactly solvable chaos --Logic from dynamics --System identification using chaos --Characterization and optimization of a chaotic LADAR system for high resolution range.The vulnerability of chaotic communication systems to noise in transmission channel is a serious obstacle for practical applications.

Traditional signal processing techniques provide only limited possibilities for efficient filtering broadband chaotic signals. In this paper, we provide a comparative study of several denoising and filtering approaches: a recursive IIR filter, a median filter, a.Abstract: Chaotic signals attracted the attention among researchers because of their rich dynamics and their random-like behavior.

What has been missing so far is an appropriate characterization of chaotic systems from a signal-processing point of view. This paper demonstrates that the framework of symbolic dynamics gives the possibility to partition the infinite number of finite-length.