Recursive identification of time-varying systems: self-tuning and matrix RLS algorithms

DOI10.1016/j.sysconle.2014.01.004zbMath1288.93088OpenAlexW2091990646MaRDI QIDQ2454068

Jian-Shu Li, Yuanjin Zheng, Zhiping Lin

Publication date: 12 June 2014

Published in: Systems \& Control Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.sysconle.2014.01.004

zbMATH Keywords

recursive identificationtime-varying systemself-tuningrecursive least squares (RLS) algorithmmatrix forgetting factor RLS algorithm

Mathematics Subject Classification ID

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

Related Items (11)

Adaptive state estimation of state-affine systems with unknown time-varying parameters ⋮ Multi-step-length gradient iterative algorithm for equation-error type models ⋮ Recursive parameter estimation algorithm for multivariate output-error systems ⋮ Recursive least squares and multi-innovation stochastic gradient parameter estimation methods for signal modeling ⋮ Discrete-time adaptive controller based on non-switch reaching condition and compact system dynamic estimator ⋮ Modified Kalman filtering based multi-step-length gradient iterative algorithm for ARX models with random missing outputs ⋮ Discontinuous gradient algorithm for finite-time estimation of time-varying parameters ⋮ Iterative parameter identification algorithms for the generalized time‐varying system with a measurable disturbance vector ⋮ Robust adaptive parameter estimator design for a multi-sinusoidal signal with fixed-time stability and guaranteed prescribed performance boundary of estimation error ⋮ Some stochastic gradient algorithms for Hammerstein systems with piecewise linearity ⋮ Global convergence of the EM algorithm for ARX models with uncertain communication channels

Cites Work

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• Identification of errors-in-variables systems with ARMA observation noises
• Recursive least-squares and accelerated convergence in stochastic approximation schemes
• Matrix forgetting factor
• Matrix forgetting factor with adaptation
• Performance bounds of forgetting factor least-squares algorithms for time-varying systems with finite measurement data
• New Improved Recursive Least-Squares Adaptive-Filtering Algorithms
• Gradient-based variable forgetting factor RLS algorithm in time-varying environments

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