Identification of stochastic nonlinear models using optimal estimating functions

DOI10.1016/j.automatica.2020.109055zbMath1451.93401OpenAlexW3002574829MaRDI QIDQ2207186

Mohamed Rasheed-Hilmy Abdalmoaty, Håkan Hjalmarsson

Publication date: 22 October 2020

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.automatica.2020.109055

zbMATH Keywords

system identificationparameter estimationnonlinear modelsstochastic systemsprediction error methods

Mathematics Subject Classification ID

Nonlinear systems in control theory (93C10) Estimation and detection in stochastic control theory (93E10) Identification in stochastic control theory (93E12)

Related Items (1)

Parameter estimation for nonlinear Volterra systems by using the multi-innovation identification theory and tensor decomposition

Uses Software

• System Identification Toolbox

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• On particle methods for parameter estimation in state-space models
• System identification of nonlinear state-space models
• On covariance function tests used in system identification
• Identification of nonlinear systems using polynomial nonlinear state space models
• Block-oriented nonlinear system identification
• Time series: Theory and methods
• Dynamic system identification. Experiment design and data analysis
• Asymptotics of estimating equations under natural conditions.
• Quasi-likelihood and its application. A general approach to optimal parameter estimation
• Identification of block-oriented nonlinear systems starting from linear approximations: a survey
• An improved method for Wiener-Hammerstein system identification based on the fractional approach
• An extension of quasi-likelihood estimation
• Nonlinear black-box modeling in system identification: A unified overview
• Nonlinear black-box models in system identification: Mathematical foundations
• Identification of Hammerstein-Wiener models
• Initializing Wiener-Hammerstein models based on partitioning of the best linear approximation
• Linear prediction error methods for stochastic nonlinear models
• Maximum likelihood identification of Wiener models
• Estimating linear time-invariant models of nonlinear time-varying systems
• Structure identification of nonlinear dynamic systems - A survey on input/output approaches
• Nonlinear System Identification
• Linear System Identification in a Nonlinear Setting: Nonparametric Analysis of the Nonlinear Distortions and Their Impact on the Best Linear Approximation
• The foundations of finite sample estimation in stochastic processes
• On the efficiency of quasi-likelihood estimation
• On linear and quadratic estimating functions
• Quasi-Likelihood and Optimal Estimation, Correspondent Paper
• Some observations on instrumental variable methods of time-series analysis
• Convergence analysis of parametric identification methods
• Frequency domain system identification with missing data
• Nonlinear System Identification: A User-Oriented Road Map
• On Fisher's Bound for Asymptotic Variances
• An Optimum Property of Regular Maximum Likelihood Estimation
• Maximum Likelihood Estimation of Misspecified Models

This page was built for publication: Identification of stochastic nonlinear models using optimal estimating functions