Auxiliary model based multi-innovation extended stochastic gradient parameter estimation with colored measurement noises
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Publication:1032409
DOI10.1016/j.sigpro.2009.03.020zbMath1178.94137OpenAlexW2053863904MaRDI QIDQ1032409
Guangjun Liu, Feng Ding, Peter Xiaoping Liu
Publication date: 26 October 2009
Published in: Signal Processing (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.sigpro.2009.03.020
parameter estimationmulti-innovation identification theorystochastic gradientLMSoutput error modelsoutput error moving average (OEMA) models
Parametric inference (62F99) Estimation and detection in stochastic control theory (93E10) Detection theory in information and communication theory (94A13)
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Cites Work
- Combined parameter and output estimation of dual-rate systems using an auxiliary model
- Performance analysis of multi-innovation gradient type identification methods
- Multi-innovation least squares identification methods based on the auxiliary model for MISO systems
- Some further results on blind identification of MIMO FIR channels via second-order statistics
- Identification for multirate multi-input systems using the multi-innovation identification theory
- Self-tuning control based on multi-innovation stochastic gradient parameter estimation
- Reconstruction of continuous-time systems from their non-uniformly sampled discrete-time systems
- Recursive output error identification algorithms theory and evaluation
- Analysis of an output error identification algorithm
- Variable step-size LMS algorithm with a quotient form
- Estimation of the parameters of multichannel autoregressive signals from noisy observations
- Extended stochastic gradient identification algorithms for Hammerstein-Wiener ARMAX systems
- Identification of dual-rate systems based on finite impulse response models
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