Modeling a nonlinear process using the exponential autoregressive time series model

DOI10.1007/s11071-018-4677-0zbMath1432.37109OpenAlexW2902560401WikidataQ115603126 ScholiaQ115603126MaRDI QIDQ2308132

Huan Xu, Feng Ding, Erfu Yang

Publication date: 25 March 2020

Published in: Nonlinear Dynamics (Search for Journal in Brave)

Full work available at URL: https://strathprints.strath.ac.uk/73602/

zbMATH Keywords

parameter estimationhierarchical identificationmulti-innovation identificationnegative gradient search

Mathematics Subject Classification ID

Parametric hypothesis testing (62F03) Point estimation (62F10) Time series analysis of dynamical systems (37M10)

Related Items (11)

Two‐stage recursive identification algorithms for a class of nonlinear time series models with colored noise ⋮ Three‐stage multi‐innovation parameter estimation for an exponential autoregressive time‐series model with moving average noise by using the data filtering technique ⋮ Fitting the exponential autoregressive model through recursive search ⋮ Improved gradient descent algorithms for time-delay rational state-space systems: intelligent search method and momentum method ⋮ Moving horizon estimation for multirate systems with time-varying time-delays ⋮ Highly computationally efficient state filter based on the delta operator ⋮ On some parameter estimation algorithms for the nonlinear exponential autoregressive model ⋮ Maximum likelihood gradient identification for multivariate equation‐error moving average systems using the multi‐innovation theory ⋮ State estimation for bilinear systems through minimizing the covariance matrix of the state estimation errors ⋮ Two-stage multi-innovation stochastic gradient algorithm for multivariate output-error ARMA systems based on the auxiliary model ⋮ Maximum likelihood-based recursive least-squares estimation for multivariable systems using the data filtering technique

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