Recovering map static nonlinearities from chaotic data using dynamical models
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Publication:1349352
DOI10.1016/S0167-2789(96)00185-6zbMath0888.93004OpenAlexW1970887889MaRDI QIDQ1349352
Publication date: 5 February 1997
Published in: Physica D (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/s0167-2789(96)00185-6
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Related Items (2)
Structure-selection techniques applied to continuous-time nonlinear models ⋮ Detecting the unstable periodic orbits of chaotic nonautonomous systems with an approximate global Poincaré map
Uses Software
Cites Work
- Identification and prediction of low dimensional dynamics
- Extraction of dynamical equations from chaotic data
- Nonlinear prediction of chaotic time series
- Dynamical effects of overparametrization in nonlinear models
- On selecting models for nonlinear time series
- Input-output parametric models for non-linear systems Part II: stochastic non-linear systems
- Orthogonal parameter estimation algorithm for non-linear stochastic systems
- A discrete ARMA model for nonlinear system identification
- Orthogonal least squares methods and their application to non-linear system identification
- GLOBAL DYNAMICAL EQUATIONS AND LYAPUNOV EXPONENTS FROM NOISY CHAOTIC TIME SERIES
- PARSIMONIOUS DYNAMICAL RECONSTRUCTION
- VALIDATING IDENTIFIED NONLINEAR MODELS WITH CHAOTIC DYNAMICS
- SYSTEM IDENTIFICATION AND MODEL-BASED CONTROL OF A CHAOTIC SYSTEM
- SOME REMARKS ON STRUCTURE SELECTION FOR NONLINEAR MODELS
- GLOBAL NONLINEAR POLYNOMIAL MODELS: STRUCTURE, TERM CLUSTERS AND FIXED POINTS
- RETRIEVING DYNAMICAL INVARIANTS FROM CHAOTIC DATA USING NARMAX MODELS
- Identification of MIMO non-linear systems using a forward-regression orthogonal estimator
- Improved structure selection for nonlinear models based on term clustering
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