A novel weight function-based robust iterative learning identification method for discrete Box-Jenkins models with Student's \(t\)-distribution noises

DOI10.1016/j.jfranklin.2017.10.021zbMath1380.93268OpenAlexW2767845335MaRDI QIDQ682739

Zhu Wang, Xiong-Lin Luo

Publication date: 5 February 2018

Published in: Journal of the Franklin Institute (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jfranklin.2017.10.021

zbMATH Keywords

robust parameter estimationdiscrete Box-Jenkins modelsiterative least squares procedureStudent's \(t\)-distribution noisesweight function-based robust iterative learning identification

Mathematics Subject Classification ID

Estimation and detection in stochastic control theory (93E10) Least squares and related methods for stochastic control systems (93E24) Identification in stochastic control theory (93E12)

Related Items (2)

Particle swarm optimization iterative identification algorithm and gradient iterative identification algorithm for Wiener systems with colored noise ⋮ Iterative learning identification for a class of parabolic distributed parameter systems

Cites Work

• The recursive least squares identification algorithm for a class of Wiener nonlinear systems
• Hierarchical gradient parameter estimation algorithm for Hammerstein nonlinear systems using the key term separation principle
• Decomposition based least squares iterative identification algorithm for multivariate pseudo-linear ARMA systems using the data filtering
• Parameter estimation algorithms for Hammerstein output error systems using Levenberg-Marquardt optimization method with varying interval measurements
• Gradient-based iterative parameter estimation for Box-Jenkins systems
• Box-jenkins identification revisited-part III: multivariable systems
• Box-Jenkins continuous-time modeling
• Performance analysis of the recursive parameter estimation algorithms for multivariable Box-Jenkins systems
• Least squares based iterative algorithm for pseudo-linear autoregressive moving average systems using the data filtering technique
• An auxiliary model based least squares algorithm for a dual-rate state space system with time-delay using the data filtering
• Maximum likelihood least squares identification method for input nonlinear finite impulse response moving average systems
• Combined state and least squares parameter estimation algorithms for dynamic systems
• The filtering based maximum likelihood recursive least squares estimation for multiple-input single-output systems
• Application of the Newton iteration algorithm to the parameter estimation for dynamical systems
• Data filtering based recursive least squares algorithm for Hammerstein systems using the key-term separation principle
• Box-Jenkins identification revisited. I: Theory
• Box-Jenkins identification revisited. II: Applications
• Adaptive joint detection and decoding in flat-fading channels via mixture Kalman filtering
• A New Robust Estimation Method for ARMA Models
• Kalman state filtering based least squares iterative parameter estimation for observer canonical state space systems using decomposition

This page was built for publication: A novel weight function-based robust iterative learning identification method for discrete Box-Jenkins models with Student's \(t\)-distribution noises