The data filtering based generalized stochastic gradient parameter estimation algorithms for multivariate output-error autoregressive systems using the auxiliary model

DOI10.1007/s11045-017-0529-1zbMath1458.93245OpenAlexW2766443968MaRDI QIDQ784691

Qinyao Liu, Feng Ding

Publication date: 3 August 2020

Published in: Multidimensional Systems and Signal Processing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11045-017-0529-1

zbMATH Keywords

parameter estimationfiltering techniqueauxiliary modelmultivariate systemmulti-innovation identification

Mathematics Subject Classification ID

Filtering in stochastic control theory (93E11) Multivariable systems, multidimensional control systems (93C35) Estimation and detection in stochastic control theory (93E10)

Related Items (3)

Collaborative linear dynamical system identification by scarce relevant/irrelevant observations ⋮ Iterative identification for multivariable systems with time-delays based on basis pursuit de-noising and auxiliary model ⋮ The filtering based auxiliary model generalized extended stochastic gradient identification for a multivariate output-error system with autoregressive moving average noise using the multi-innovation theory

Cites Work

• A proportional differential control method for a time-delay system using the Taylor expansion approximation
• Maximum likelihood least squares identification method for active noise control systems with autoregressive moving average noise
• Event-triggered leader-following tracking control for multivariable multi-agent systems
• Linear approximation and identification of MIMO Wiener-Hammerstein systems
• Novel data filtering based parameter identification for multiple-input multiple-output systems using the auxiliary model
• Decomposition based least squares iterative identification algorithm for multivariate pseudo-linear ARMA systems using the data filtering
• Bias compensation principle based recursive least squares identification method for Hammerstein nonlinear systems
• New gradient based identification methods for multivariate pseudo-linear systems using the multi-innovation and the data filtering
• Distributed recursive filtering for stochastic systems under uniform quantizations and deception attacks through sensor networks
• Parameterization and identification of multivariable state-space systems: a canonical approach
• Performance analysis of stochastic gradient algorithms under weak conditions
• Recursive least squares identification of hybrid Box-Jenkins model structure in open-loop and closed-loop
• Synchronization of uncertain chaotic systems via complete-adaptive-impulsive controls
• Least-squares-based iterative and gradient-based iterative estimation algorithms for bilinear systems
• Subspace identification for closed-loop 2-D separable-in-denominator systems
• A novel accelerated artificial bee colony algorithm for optimal design of two dimensional FIR filter
• The maximum likelihood least squares based iterative estimation algorithm for bilinear systems with autoregressive moving average noise
• Application of the Newton iteration algorithm to the parameter estimation for dynamical systems
• Recursive least squares and multi-innovation stochastic gradient parameter estimation methods for signal modeling
• Multiperiodicity and exponential attractivity of neural networks with mixed delays
• The gradient-based iterative estimation algorithms for bilinear systems with autoregressive noise
• Robust state estimation for two-dimensional stochastic time-delay systems with missing measurements and sensor saturation
• Recursive Identification of MIMO Wiener Systems

This page was built for publication: The data filtering based generalized stochastic gradient parameter estimation algorithms for multivariate output-error autoregressive systems using the auxiliary model